| 2024 | $C^2M^3$: Cycle-Consistent Multi-Model Merging. Donato Crisostomi, Marco Fumero, Daniele Baieri, Florian Bernard, Emanuele Rodolà |
| 2024 | $SE(3)$ Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation. Yinshuang Xu, Dian Chen, Katherine Liu, Sergey Zakharov, Rares Ambrus, Kostas Daniilidis, Vitor Guizilini |
| 2024 | $\epsilon$-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise. Jialiang Wang, Xiong Zhou, Deming Zhai, Junjun Jiang, Xiangyang Ji, Xianming Liu |
| 2024 | $\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials. Kuzma Khrabrov, Anton Ber, Artem Tsypin, Konstantin Ushenin, Egor Rumiantsev, Alexander Telepov, Dmitry Protasov, Ilya Shenbin, Anton Alekseev, Mikhail Shirokikh, Sergey I. Nikolenko, Elena Tutubalina, Artur Kadurin |
| 2024 | $\text{Di}^2\text{Pose}$: Discrete Diffusion Model for Occluded 3D Human Pose Estimation. Weiquan Wang, Jun Xiao, Chunping Wang, Wei Liu, Zhao Wang, Long Chen |
| 2024 | (FL) Seungjoo Lee, Thanh-Long V. Le, Jaemin Shin, Sung-Ju Lee |
| 2024 | 2D-OOB: Attributing Data Contribution Through Joint Valuation Framework. Yifan Sun, Jingyan Shen, Yongchan Kwon |
| 2024 | 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution. Kai Liu, Haotong Qin, Yong Guo, Xin Yuan, Linghe Kong, Guihai Chen, Yulun Zhang |
| 2024 | 3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability. Baohao Liao, Christof Monz |
| 2024 | 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction. Jongmin Lee, Minsu Cho |
| 2024 | 3D Focusing-and-Matching Network for Multi-Instance Point Cloud Registration. Liyuan Zhang, Le Hui, Qi Liu, Bo Li, Yuchao Dai |
| 2024 | 3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning. Zhifan Ye, Chenxi Wan, Chaojian Li, Jihoon Hong, Sixu Li, Leshu Li, Yongan Zhang, Yingyan (Celine) Lin |
| 2024 | 3D Gaussian Splatting as Markov Chain Monte Carlo. Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Yang-Che Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi |
| 2024 | 3D Structure Prediction of Atomic Systems with Flow-based Direct Preference Optimization. Rui Jiao, Xiangzhe Kong, Wenbing Huang, Yang Liu |
| 2024 | 3DCoMPaT200: Language Grounded Large-Scale 3D Vision Dataset for Compositional Recognition. Mahmoud Ahmed, Xiang Li, Arpit Prajapati, Mohamed Elhoseiny |
| 2024 | 3DET-Mamba: Causal Sequence Modelling for End-to-End 3D Object Detection. Mingsheng Li, Jiakang Yuan, Sijin Chen, Lin Zhang, Anyu Zhu, Xin Chen, Tao Chen |
| 2024 | 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors. Xi Liu, Chaoyi Zhou, Siyu Huang |
| 2024 | 4+3 Phases of Compute-Optimal Neural Scaling Laws. Elliot Paquette, Courtney Paquette, Lechao Xiao, Jeffrey Pennington |
| 2024 | 4-bit Shampoo for Memory-Efficient Network Training. Sike Wang, Pan Zhou, Jia Li, Hua Huang |
| 2024 | 4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization. Mijeong Kim, Jongwoo Lim, Bohyung Han |
| 2024 | 4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs. Minjie Wang, Quan Gan, David Wipf, Zheng Zhang, Christos Faloutsos, Weinan Zhang, Muhan Zhang, Zhenkun Cai, Jiahang Li, Zunyao Mao, Yakun Song, Jianheng Tang, Yanlin Zhang, Guang Yang, Chuan Lei, Xiao Qin, Ning Li, Han Zhang, Yanbo Wang, Zizhao Zhang |
| 2024 | 4Diffusion: Multi-view Video Diffusion Model for 4D Generation. Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu, Yunhong Wang, Yu Qiao |
| 2024 | 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities. Roman Bachmann, Oguzhan Fatih Kar, David Mizrahi, Ali Garjani, Mingfei Gao, David Griffiths, Jiaming Hu, Afshin Dehghan, Amir Zamir |
| 2024 | 4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models. Heng Yu, Chaoyang Wang, Peiye Zhuang, Willi Menapace, Aliaksandr Siarohin, Junli Cao, László A. Jeni, Sergey Tulyakov, Hsin-Ying Lee |
| 2024 | A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings. Disha Makhija, Joydeep Ghosh, Nhat Ho |
| 2024 | A Bayesian Approach to Data Point Selection. Xinnuo Xu, Minyoung Kim, Royson Lee, Brais Martínez, Timothy M. Hospedales |
| 2024 | A Benchmark Dataset for Event-Guided Human Pose Estimation and Tracking in Extreme Conditions. Hoonhee Cho, Taewoo Kim, Yuhwan Jeong, Kuk-Jin Yoon |
| 2024 | A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasets. Kyungeun Lee, Wonjong Rhee |
| 2024 | A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays. Saeed Masoudian, Julian Zimmert, Yevgeny Seldin |
| 2024 | A Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers. Xin Zou, Zhengyu Zhou, Jingyuan Xu, Weiwei Liu |
| 2024 | A Canonicalization Perspective on Invariant and Equivariant Learning. George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang |
| 2024 | A Careful Examination of Large Language Model Performance on Grade School Arithmetic. Hugh Zhang, Jeff Da, Dean Lee, Vaughn Robinson, Catherine Wu, William Song, Tiffany Zhao, Pranav Raja, Charlotte Zhuang, Dylan Slack, Qin Lyu, Sean Hendryx, Russell Kaplan, Michele Lunati, Summer Yue |
| 2024 | A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding Optimization. Chieh-Yun Chen, Chiang Tseng, Li-Wu Tsao, Hong-Han Shuai |
| 2024 | A Closer Look at AUROC and AUPRC under Class Imbalance. Matthew B. A. McDermott, Haoran Zhang, Lasse Hyldig Hansen, Giovanni Angelotti, Jack Gallifant |
| 2024 | A Closer Look at the CLS Token for Cross-Domain Few-Shot Learning. Yixiong Zou, Shuai Yi, Yuhua Li, Ruixuan Li |
| 2024 | A Combinatorial Algorithm for the Semi-Discrete Optimal Transport Problem. Pankaj K. Agarwal, Sharath Raghvendra, Pouyan Shirzadian, Keegan Yao |
| 2024 | A Compositional Atlas for Algebraic Circuits. Benjie Wang, Denis Deratani Mauá, Guy Van den Broeck, YooJung Choi |
| 2024 | A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression. Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, David Belius |
| 2024 | A Concept-Based Explainability Framework for Large Multimodal Models. Jayneel Parekh, Pegah Khayatan, Mustafa Shukor, Alasdair Newson, Matthieu Cord |
| 2024 | A Consistency-Aware Spot-Guided Transformer for Versatile and Hierarchical Point Cloud Registration. Renlang Huang, Yufan Tang, Jiming Chen, Liang Li |
| 2024 | A Critical Evaluation of AI Feedback for Aligning Large Language Models. Archit Sharma, Sedrick Scott Keh, Eric Mitchell, Chelsea Finn, Kushal Arora, Thomas Kollar |
| 2024 | A Cross-Domain Benchmark for Active Learning. Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme |
| 2024 | A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data. Andrej Tschalzev, Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt |
| 2024 | A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health. Nikhil Behari, Edwin Zhang, Yunfan Zhao, Aparna Taneja, Dheeraj Nagaraj, Milind Tambe |
| 2024 | A Fast Convoluted Story: Scaling Probabilistic Inference for Integer Arithmetics. Lennert De Smet, Pedro Zuidberg Dos Martires |
| 2024 | A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening. Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca, Haggai Maron |
| 2024 | A Foundation Model for Zero-shot Logical Query Reasoning. Michael Galkin, Jincheng Zhou, Bruno Ribeiro, Jian Tang, Zhaocheng Zhu |
| 2024 | A Framework for Bilevel Optimization on Riemannian Manifolds. Andi Han, Bamdev Mishra, Pratik Kumar Jawanpuria, Akiko Takeda |
| 2024 | A Full-duplex Speech Dialogue Scheme Based On Large Language Model. Peng Wang, Songshuo Lu, Yaohua Tang, Sijie Yan, Wei Xia, Yuanjun Xiong |
| 2024 | A Functional Extension of Semi-Structured Networks. David Rügamer, Bernard X. W. Liew, Zainab Altai, Almond Stöcker |
| 2024 | A General Protocol to Probe Large Vision Models for 3D Physical Understanding. Guanqi Zhan, Chuanxia Zheng, Weidi Xie, Andrew Zisserman |
| 2024 | A Generative Model of Symmetry Transformations. James Urquhart Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato |
| 2024 | A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models. Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem |
| 2024 | A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding. Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian, Jingbo Liu, Hujun Bao, Zhaopeng Cui, Hongsheng Li, Guofeng Zhang |
| 2024 | A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization. Yizun Lin, Zhao-Rong Lai, Cheng Li |
| 2024 | A Gradient Accumulation Method for Dense Retriever under Memory Constraint. Jaehee Kim, Yukyung Lee, Pilsung Kang |
| 2024 | A Hitchhiker's Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning. Niki Maria Foteinopoulou, Enjie Ghorbel, Djamila Aouada |
| 2024 | A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy. Puning Zhao, Lifeng Lai, Li Shen, Qingming Li, Jiafei Wu, Zhe Liu |
| 2024 | A Kernel Perspective on Distillation-based Collaborative Learning. Sejun Park, Kihun Hong, Ganguk Hwang |
| 2024 | A Label is Worth A Thousand Images in Dataset Distillation. Tian Qin, Zhiwei Deng, David Alvarez-Melis |
| 2024 | A Large-Scale Human-Centric Benchmark for Referring Expression Comprehension in the LMM Era. Fangyun Wei, Jinjing Zhao, Kun Yan, Hongyang Zhang, Chang Xu |
| 2024 | A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks. Xiaolei Liu, Shaoshuai Li, Kaixin Gao, Binfeng Wang |
| 2024 | A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs. Haoxuan Li, Yue Liu, Zhi Geng, Kun Zhang |
| 2024 | A Metalearned Neural Circuit for Nonparametric Bayesian Inference. Jake Snell, Gianluca M. Bencomo, Tom Griffiths |
| 2024 | A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning. Jacob Adkins, Michael Bowling, Adam White |
| 2024 | A Modular Conditional Diffusion Framework for Image Reconstruction. Magauiya Zhussip, Iaroslav Koshelev, Stamatios Lefkimmiatis |
| 2024 | A Motion-aware Spatio-temporal Graph for Video Salient Object Ranking. Hao Chen, Yufei Zhu, Yongjian Deng |
| 2024 | A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise. Ilias Diakonikolas, Nikos Zarifis |
| 2024 | A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation. Heyang Zhao, Jiafan He, Quanquan Gu |
| 2024 | A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models. Shivvrat Arya, Tahrima Rahman, Vibhav Gogate |
| 2024 | A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts. Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, Andrea Passerini |
| 2024 | A New Multi-Source Light Detection Benchmark and Semi-Supervised Focal Light Detection. Jae-Yong Baek, Yong-Sang Yoo, Seung Hwan Bae |
| 2024 | A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning. Julia B. Nakhleh, Joseph Shenouda, Robert D. Nowak |
| 2024 | A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding. Xinhai Zhang, Xingye Qiao |
| 2024 | A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation. Jer Pelhan, Alan Lukezic, Vitjan Zavrtanik, Matej Kristan |
| 2024 | A PID Controller Approach for Adaptive Probability-dependent Gradient Decay in Model Calibration. Siyuan Zhang, Linbo Xie |
| 2024 | A Pairwise Pseudo-likelihood Approach for Matrix Completion with Informative Missingness. Jiangyuan Li, Jiayi Wang, Raymond K. W. Wong, Kwun Chuen Gary Chan |
| 2024 | A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention. Hugo Cui, Freya Behrens, Florent Krzakala, Lenka Zdeborová |
| 2024 | A Polar coordinate system represents syntax in large language models. Pablo Diego-Simón, Stéphane d'Ascoli, Emmanuel Chemla, Yair Lakretz, Jean-Remi King |
| 2024 | A Practitioner's Guide to Real-World Continual Multimodal Pretraining. Vishaal Udandarao, Karsten Roth, Sebastian Dziadzio, Ameya Prabhu, Mehdi Cherti, Oriol Vinyals, Olivier J. Hénaff, Samuel Albanie, Zeynep Akata, Matthias Bethge |
| 2024 | A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints. Liuyuan Jiang, Quan Xiao, Victor Tenorio, Fernando Real-Rojas, Antonio G. Marques, Tianyi Chen |
| 2024 | A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning. Yuanning Cui, Zequn Sun, Wei Hu |
| 2024 | A Recipe for Charge Density Prediction. Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola |
| 2024 | A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics. Puze Liu, Jonas Günster, Niklas Funk, Simon Gröger, Dong Chen, Haitham Bou-Ammar, Julius Jankowski, Ante Maric, Sylvain Calinon, Andrej Orsula, Miguel S. Olivares-Méndez, Hongyi Zhou, Rudolf Lioutikov, Gerhard Neumann, Amarildo Likmeta, Amirhossein Zhalehmehrabi, Thomas Bonenfant, Marcello Restelli, Davide Tateo, Ziyuan Liu, Jan R. Peters |
| 2024 | A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models. Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda, Ryotaro Tamura, Akihiro Imura |
| 2024 | A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers. Ye He, Alireza Mousavi Hosseini, Krishnakumar Balasubramanian, Murat A. Erdogdu |
| 2024 | A Siamese Transformer with Hierarchical Refinement for Lane Detection. Zinan Lv, Dong Han, Wenzhe Wang, Danny Z. Chen |
| 2024 | A Simple Framework for Generalization in Visual RL under Dynamic Scene Perturbations. Wonil Song, Hyesong Choi, Kwanghoon Sohn, Dongbo Min |
| 2024 | A Simple Image Segmentation Framework via In-Context Examples. Yang Liu, Chenchen Jing, Hengtao Li, Muzhi Zhu, Hao Chen, Xinlong Wang, Chunhua Shen |
| 2024 | A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective. Yeonsung Jung, Jaeyun Song, June Yong Yang, Jin-Hwa Kim, Sungyub Kim, Eunho Yang |
| 2024 | A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds. Taira Tsuchiya, Shinji Ito |
| 2024 | A Simple and Optimal Approach for Universal Online Learning with Gradient Variations. Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou |
| 2024 | A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences. Mingjia Li, Shuo Liu, Hong Qian, Aimin Zhou |
| 2024 | A Simple yet Universal Framework for Depth Completion. Jin-Hwi Park, Hae-Gon Jeon |
| 2024 | A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data. Adrian Remonda, Nicklas Hansen, Ayoub Raji, Nicola Musiu, Marko Bertogna, Eduardo E. Veas, Xiaolong Wang |
| 2024 | A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training. Jie Ji, Gen Li, Jingjing Fu, Fatemeh Afghah, Linke Guo, Xiaoyong Yuan, Xiaolong Ma |
| 2024 | A Sober Look at the Robustness of CLIPs to Spurious Features. Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang |
| 2024 | A StrongREJECT for Empty Jailbreaks. Alexandra Souly, Qingyuan Lu, Dillon Bowen, Tu Trinh, Elvis Hsieh, Sana Pandey, Pieter Abbeel, Justin Svegliato, Scott Emmons, Olivia Watkins, Sam Toyer |
| 2024 | A Structure-Aware Framework for Learning Device Placements on Computation Graphs. Shukai Duan, Heng Ping, Nikos Kanakaris, Xiongye Xiao, Panagiotis Kyriakis, Nesreen K. Ahmed, Peiyu Zhang, Guixiang Ma, Mihai Capota, Shahin Nazarian, Theodore L. Willke, Paul Bogdan |
| 2024 | A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning. Arthur Juliani, Jordan T. Ash |
| 2024 | A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation. Tomoya Sakai, Haoxiang Qiu, Takayuki Katsuki, Daiki Kimura, Takayuki Osogami, Tadanobu Inoue |
| 2024 | A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm. Tianchi Liao, Lele Fu, Jialong Chen, Zhen Wang, Zibin Zheng, Chuan Chen |
| 2024 | A Synthetic Dataset for Personal Attribute Inference. Hanna Yukhymenko, Robin Staab, Mark Vero, Martin T. Vechev |
| 2024 | A Systematic Review of NeurIPS Dataset Management Practices. Yiwei Wu, Leah Ajmani, Shayne Longpre, Hanlin Li |
| 2024 | A Taxonomy of Challenges to Curating Fair Datasets. Dora Zhao, Morgan Klaus Scheuerman, Pooja Chitre, Jerone Theodore Alexander Andrews, Georgia Panagiotidou, Shawn Walker, Kathleen H. Pine, Alice Xiang |
| 2024 | A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis. Yue Yang, Mona Gandhi, Yufei Wang, Yifan Wu, Michael S. Yao, Chris Callison-Burch, James C. Gee, Mark Yatskar |
| 2024 | A Theoretical Perspective for Speculative Decoding Algorithm. Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang |
| 2024 | A Theoretical Understanding of Self-Correction through In-context Alignment. Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, Yisen Wang |
| 2024 | A Theory of Optimistically Universal Online Learnability for General Concept Classes. Steve Hanneke, Hongao Wang |
| 2024 | A Topology-aware Graph Coarsening Framework for Continual Graph Learning. Xiaoxue Han, Zhuo Feng, Yue Ning |
| 2024 | A Tractable Inference Perspective of Offline RL. Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang |
| 2024 | A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits. Junghyun Lee, Se-Young Yun, Kwang-Sung Jun |
| 2024 | A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks. Hoin Jung, Taeuk Jang, Xiaoqian Wang |
| 2024 | A Unified Framework for 3D Scene Understanding. Wei Xu, Chunsheng Shi, Sifan Tu, Xin Zhou, Dingkang Liang, Xiang Bai |
| 2024 | A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch. Yue Wang, Zhongchang Sun, Shaofeng Zou |
| 2024 | A Unifying Normative Framework of Decision Confidence. Amelia Johnson, Michael A. Buice, Koosha Khalvati |
| 2024 | A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems. Mohammad-Amin Charusaie, Samira Samadi |
| 2024 | A Universal Growth Rate for Learning with Smooth Surrogate Losses. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2024 | A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation. Gwanghyun Kim, Alonso Martinez, Yu-Chuan Su, Brendan Jou, José Lezama, Agrim Gupta, Lijun Yu, Lu Jiang, Aren Jansen, Jacob Walker, Krishna Somandepalli |
| 2024 | A Walsh Hadamard Derived Linear Vector Symbolic Architecture. Mohammad Mahmudul Alam, Alexander Oberle, Edward Raff, Stella Biderman, Tim Oates, James Holt |
| 2024 | A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types. Artur Szalata, Andrew Benz, Robrecht Cannoodt, Mauricio Cortes, Jason Fong, Sunil Kuppasani, Richard Lieberman, Tianyu Liu, Javier Mas-Rosario, Rico Meinl, Jalil Nourisa, Jared Tumiel, Tin M. Tunjic, Mengbo Wang, Noah Weber, Hongyu Zhao, Benedict Anchang, Fabian J. Theis, Malte Luecken, Daniel Burkhardt |
| 2024 | A distributional simplicity bias in the learning dynamics of transformers. Riccardo Rende, Federica Gerace, Alessandro Laio, Sebastian Goldt |
| 2024 | A generalized neural tangent kernel for surrogate gradient learning. Luke Eilers, Raoul-Martin Memmesheimer, Sven Goedeke |
| 2024 | A hierarchical decomposition for explaining ML performance discrepancies. Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann, Jean Feng |
| 2024 | A probability contrastive learning framework for 3D molecular representation learning. Jiayu Qin, Jian Chen, Rohan Sharma, Jingchen Sun, Changyou Chen |
| 2024 | A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness. Yuri Kinoshita, Taro Toyoizumi |
| 2024 | A robust inlier identification algorithm for point cloud registration via 𝓁 Yinuo Jiang, Xiuchuan Tang, Cheng Cheng, Ye Yuan |
| 2024 | A scalable generative model for dynamical system reconstruction from neuroimaging data. Eric Volkmann, Alena Brändle, Daniel Durstewitz, Georgia Koppe |
| 2024 | A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences. Miguel González Duque, Richard Michael, Simon Bartels, Yevgen Zainchkovskyy, Søren Hauberg, Wouter Boomsma |
| 2024 | A teacher-teacher framework for clinical language representation learning. Feiqing Huang, Shenghan Zhang, Sara Morini Sweet, Tianxi Cai |
| 2024 | A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning. Tom Yan, Zachary C. Lipton |
| 2024 | A theoretical design of concept sets: improving the predictability of concept bottleneck models. Max Ruiz Luyten, Mihaela van der Schaar |
| 2024 | A two-scale Complexity Measure for Deep Learning Models. Massimiliano Datres, Gian Paolo Leonardi, Alessio Figalli, David Sutter |
| 2024 | A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis. Lei Huang, Lei Xiong, Na Sun, Zunpeng Liu, Ka-Chun Wong, Manolis Kellis |
| 2024 | A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs. Yan Sun, Li Shen, Dacheng Tao |
| 2024 | A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective. Yunpeng Qing, Shunyu Liu, Jingyuan Cong, Kaixuan Chen, Yihe Zhou, Mingli Song |
| 2024 | ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods. MaryBeth Defrance, Maarten Buyl, Tijl De Bie |
| 2024 | ACES: Generating a Diversity of Challenging Programming Puzzles with Autotelic Generative Models. Julien Pourcel, Cédric Colas, Gaia Molinaro, Pierre-Yves Oudeyer, Laetitia Teodorescu |
| 2024 | ACFun: Abstract-Concrete Fusion Facial Stylization. Jiapeng Ji, Kun Wei, Ziqi Zhang, Cheng Deng |
| 2024 | ADOPT: Modified Adam Can Converge with Any β Shohei Taniguchi, Keno Harada, Gouki Minegishi, Yuta Oshima, Seong Cheol Jeong, Go Nagahara, Tomoshi Iiyama, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo |
| 2024 | AED: Adaptable Error Detection for Few-shot Imitation Policy. Jia-Fong Yeh, Kuo-Han Hung, Pang-Chi Lo, Chi-Ming Chung, Tsung-Han Wu, Hung-Ting Su, Yi-Ting Chen, Winston H. Hsu |
| 2024 | AFBench: A Large-scale Benchmark for Airfoil Design. Jian Liu, Jianyu Wu, Hairun Xie, Guoqing Zhang, Jing Wang, Wei Liu, Wanli Ouyang, Junjun Jiang, Xianming Liu, Shixiang Tang, Miao Zhang |
| 2024 | AGILE: A Novel Reinforcement Learning Framework of LLM Agents. Peiyuan Feng, Yichen He, Guanhua Huang, Yuan Lin, Hanchong Zhang, Yuchen Zhang, Hang Li |
| 2024 | AHA: Human-Assisted Out-of-Distribution Generalization and Detection. Haoyue Bai, Jifan Zhang, Robert D. Nowak |
| 2024 | AID: Attention Interpolation of Text-to-Image Diffusion. Qiyuan He, Jinghao Wang, Ziwei Liu, Angela Yao |
| 2024 | ALI-Agent: Assessing LLMs' Alignment with Human Values via Agent-based Evaluation. Jingnan Zheng, Han Wang, An Zhang, Tai D. Nguyen, Jun Sun, Tat-Seng Chua |
| 2024 | ALPINE: Unveiling The Planning Capability of Autoregressive Learning in Language Models. Siwei Wang, Yifei Shen, Shi Feng, Haoran Sun, Shang-Hua Teng, Wei Chen |
| 2024 | ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models. Xiang Meng, Kayhan Behdin, Haoyue Wang, Rahul Mazumder |
| 2024 | AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers. Jake Grigsby, Justin Sasek, Samyak Parajuli, Daniel Adebi, Amy Zhang, Yuke Zhu |
| 2024 | AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries. Irina Saparina, Mirella Lapata |
| 2024 | AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback. Jian Guan, Wei Wu, Zujie Wen, Peng Xu, Hongning Wang, Minlie Huang |
| 2024 | ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models. Yuzhe Gu, Ziwei Ji, Wenwei Zhang, Chengqi Lyu, Dahua Lin, Kai Chen |
| 2024 | ANT: Adaptive Noise Schedule for Time Series Diffusion Models. Seunghan Lee, Kibok Lee, Taeyoung Park |
| 2024 | AP-Adapter: Improving Generalization of Automatic Prompts on Unseen Text-to-Image Diffusion Models. Yuchen Fu, Zhiwei Jiang, Yuliang Liu, Cong Wang, Zexuan Deng, Zhaoling Chen, Qing Gu |
| 2024 | APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled Scores and Comments. Xin Jin, Qianqian Qiao, Yi Lu, Huaye Wang, Heng Huang, Shan Gao, Jianfei Liu, Rui Li |
| 2024 | APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs. Felix Koehler, Simon Niedermayr, Rüdiger Westermann, Nils Thuerey |
| 2024 | APIGen: Automated PIpeline for Generating Verifiable and Diverse Function-Calling Datasets. Zuxin Liu, Thai Hoang, Jianguo Zhang, Ming Zhu, Tian Lan, Shirley Kokane, Juntao Tan, Weiran Yao, Zhiwei Liu, Yihao Feng, Rithesh R. N., Liangwei Yang, Silvio Savarese, Juan Carlos Niebles, Huan Wang, Shelby Heinecke, Caiming Xiong |
| 2024 | AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties. Xiayan Ji, Anton Xue, Eric Wong, Oleg Sokolsky, Insup Lee |
| 2024 | ARC: A Generalist Graph Anomaly Detector with In-Context Learning. Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, Shirui Pan |
| 2024 | AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields. Louis Serrano, Thomas X. Wang, Etienne Le Naour, Jean-Noël Vittaut, Patrick Gallinari |
| 2024 | ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users. Guanlin Li, Kangjie Chen, Shudong Zhang, Jie Zhang, Tianwei Zhang |
| 2024 | AUC Maximization under Positive Distribution Shift. Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi, Taishi Nishiyama, Yasuhiro Fujiwara |
| 2024 | AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation. Boyu Han, Qianqian Xu, Zhiyong Yang, Shilong Bao, Peisong Wen, Yangbangyan Jiang, Qingming Huang |
| 2024 | AV-Cloud: Spatial Audio Rendering Through Audio-Visual Cloud Splatting. Mingfei Chen, Eli Shlizerman |
| 2024 | AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis. Swapnil Bhosale, Haosen Yang, Diptesh Kanojia, Jiankang Deng, Xiatian Zhu |
| 2024 | AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation. Yuhan Zhu, Yuyang Ji, Zhiyu Zhao, Gangshan Wu, Limin Wang |
| 2024 | Abductive Reasoning in Logical Credal Networks. Radu Marinescu, Junkyu Lee, Debarun Bhattacharjya, Fábio G. Cozman, Alexander G. Gray |
| 2024 | Abrupt Learning in Transformers: A Case Study on Matrix Completion. Pulkit Gopalani, Ekdeep Singh Lubana, Wei Hu |
| 2024 | Absorb & Escape: Overcoming Single Model Limitations in Generating Heterogeneous Genomic Sequences. Zehui Li, Yuhao Ni, Guoxuan Xia, William A. V. Beardall, Akashaditya Das, Guy-Bart Stan, Yiren Zhao |
| 2024 | Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation. Shreyas Chaudhari, Ameet Deshpande, Bruno C. da Silva, Philip S. Thomas |
| 2024 | Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. Yunshi Wen, Tengfei Ma, Lily Weng, Lam M. Nguyen, Anak Agung Julius |
| 2024 | Accelerated Regularized Learning in Finite N-Person Games. Kyriakos Lotidis, Angeliki Giannou, Panayotis Mertikopoulos, Nicholas Bambos |
| 2024 | Accelerating Augmentation Invariance Pretraining. Jinhong Lin, Cheng-En Wu, Yibing Wei, Pedro Morgado |
| 2024 | Accelerating Blockwise Parallel Language Models with Draft Refinement. Taehyeon Kim, Ananda Theertha Suresh, Kishore Papineni, Michael D. Riley, Sanjiv Kumar, Adrian Benton |
| 2024 | Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity. Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant M. Rotskoff |
| 2024 | Accelerating ERM for data-driven algorithm design using output-sensitive techniques. Maria-Florina Balcan, Christopher Seiler, Dravyansh Sharma |
| 2024 | Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling. Yiran Zhao, Wenyue Zheng, Tianle Cai, Do Xuan Long, Kenji Kawaguchi, Anirudh Goyal, Michael Qizhe Shieh |
| 2024 | Accelerating Matroid Optimization through Fast Imprecise Oracles. Franziska Eberle, Felix Hommelsheim, Alexander Lindermayr, Zhenwei Liu, Nicole Megow, Jens Schlöter |
| 2024 | Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play. Qi Ju, Falin Hei, Ting Feng, Dengbing Yi, Zhemei Fang, Yunfeng Luo |
| 2024 | Accelerating Non-Maximum Suppression: A Graph Theory Perspective. King-Siong Si, Lu Sun, Weizhan Zhang, Tieliang Gong, Jiahao Wang, Jiang Liu, Hao Sun |
| 2024 | Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight. Ziyuan Huang, Kaixiang Ji, Biao Gong, Zhiwu Qing, Qinglong Zhang, Kecheng Zheng, Jian Wang, Jingdong Chen, Ming Yang |
| 2024 | Accelerating Relative Entropy Coding with Space Partitioning. Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato |
| 2024 | Accelerating Transformers with Spectrum-Preserving Token Merging. Chau Tran, Duy M. H. Nguyen, Manh-Duy Nguyen, TrungTin Nguyen, Ngan Le, Pengtao Xie, Daniel Sonntag, James Y. Zou, Binh Nguyen, Mathias Niepert |
| 2024 | Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values. Aleksandr V. Lobanov, Alexander V. Gasnikov, Andrey Krasnov |
| 2024 | Accuracy is Not All You Need. Abhinav Dutta, Sanjeev Krishnan, Nipun Kwatra, Ramachandran Ramjee |
| 2024 | Accurate and Steady Inertial Pose Estimation through Sequence Structure Learning and Modulation. Yinghao Wu, Chaoran Wang, Lu Yin, Shihui Guo, Yipeng Qin |
| 2024 | Achievable Fairness on Your Data With Utility Guarantees. Muhammad Faaiz Taufiq, Jean-Francois Ton, Yang Liu |
| 2024 | Achievable distributional robustness when the robust risk is only partially identified. Julia Kostin, Nicola Gnecco, Fanny Yang |
| 2024 | Achieving Constant Regret in Linear Markov Decision Processes. Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu |
| 2024 | Achieving Domain-Independent Certified Robustness via Knowledge Continuity. Alan Sun, Chiyu Ma, Kenneth Ge, Soroush Vosoughi |
| 2024 | Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization. Ilya A. Kuruzov, Gesualdo Scutari, Alexander V. Gasnikov |
| 2024 | Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes. Yan Huang, Xiang Li, Yipeng Shen, Niao He, Jinming Xu |
| 2024 | Achieving Optimal Clustering in Gaussian Mixture Models with Anisotropic Covariance Structures. Xin Chen, Anderson Ye Zhang |
| 2024 | Achieving Õ(1/ε) Sample Complexity for Constrained Markov Decision Process. Jiashuo Jiang, Yinyu Ye |
| 2024 | Achieving Tractable Minimax Optimal Regret in Average Reward MDPs. Victor Boone, Zihan Zhang |
| 2024 | Acoustic Volume Rendering for Neural Impulse Response Fields. Zitong Lan, Chenhao Zheng, Zhiwei Zheng, Mingmin Zhao |
| 2024 | ActAnywhere: Subject-Aware Video Background Generation. Boxiao Pan, Zhan Xu, Chun-Hao Paul Huang, Krishna Kumar Singh, Yang Zhou, Leonidas J. Guibas, Jimei Yang |
| 2024 | ActFusion: a Unified Diffusion Model for Action Segmentation and Anticipation. Dayoung Gong, Suha Kwak, Minsu Cho |
| 2024 | ActSort: An active-learning accelerated cell sorting algorithm for large-scale calcium imaging datasets. Yiqi Jiang, Hakki O. Akengin, Ji Zhou, Mehmet Aslihak, Yang Li, Radoslaw Chrapkiewicz, Oscar Hernandez, Sadegh Ebrahimi, Omar Jaidar, Yanping Zhang, Hakan Inan, Christopher Miranda, Fatih Dinc, Marta Blanco-Pozo, Mark J. Schnitzer |
| 2024 | Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning. Harley Wiltzer, Marc G. Bellemare, David Meger, Patrick Shafto, Yash Jhaveri |
| 2024 | Action Imitation in Common Action Space for Customized Action Image Synthesis. Wang Lin, Jingyuan Chen, Jiaxin Shi, Zirun Guo, Yichen Zhu, Zehan Wang, Tao Jin, Zhou Zhao, Fei Wu, Shuicheng Yan, Hanwang Zhang |
| 2024 | ActionAtlas: A VideoQA Benchmark for Domain-specialized Action Recognition. Mohammadreza Salehi, Jae Sung Park, Aditya Kusupati, Ranjay Krishna, Yejin Choi, Hanna Hajishirzi, Ali Farhadi |
| 2024 | Activating Self-Attention for Multi-Scene Absolute Pose Regression. Miso Lee, Jihwan Kim, Jae-Pil Heo |
| 2024 | Activation Map Compression through Tensor Decomposition for Deep Learning. Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione, Samuel Tardieu, Van-Tam Nguyen |
| 2024 | Active Classification with Few Queries under Misspecification. Vasilis Kontonis, Mingchen Ma, Christos Tzamos |
| 2024 | Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. Syrine Belakaria, Ben Letham, Jana Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy |
| 2024 | Active Learning of General Halfspaces: Label Queries vs Membership Queries. Ilias Diakonikolas, Daniel M. Kane, Mingchen Ma |
| 2024 | Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios. Nicolás Astorga, Tennison Liu, Nabeel Seedat, Mihaela van der Schaar |
| 2024 | Active Perception for Grasp Detection via Neural Graspness Field. Haoxiang Ma, Modi Shi, Boyang Gao, Di Huang |
| 2024 | Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference. Sam Griesemer, Defu Cao, Zijun Cui, Carolina Osorio, Yan Liu |
| 2024 | Active Set Ordering. Quoc Phong Nguyen, Sunil Gupta, Svetha Venkatesh, Bryan Kian Hsiang Low, Patrick Jaillet |
| 2024 | Active learning of neural population dynamics using two-photon holographic optogenetics. Andrew Wagenmaker, Lu Mi, Marton Rozsa, Matthew S. Bull, Karel Svoboda, Kayvon Daie, Matthew D. Golub, Kevin Jamieson |
| 2024 | Active preference learning for ordering items in- and out-of-sample. Herman Bergström, Emil Carlsson, Devdatt P. Dubhashi, Fredrik D. Johansson |
| 2024 | Active, anytime-valid risk controlling prediction sets. Ziyu Xu, Nikos Karampatziakis, Paul Mineiro |
| 2024 | Ad Auctions for LLMs via Retrieval Augmented Generation. MohammadTaghi Hajiaghayi, Sébastien Lahaie, Keivan Rezaei, Suho Shin |
| 2024 | Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting. Zongjiang Shang, Ling Chen, Binqing Wu, Dongliang Cui |
| 2024 | AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies. Xixi Hu, Qiang Liu, Xingchao Liu, Bo Liu |
| 2024 | AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models. Yabin Zhang, Lei Zhang |
| 2024 | AdaNovo: Towards Robust \emph{De Novo} Peptide Sequencing in Proteomics against Data Biases. Jun Xia, Shaorong Chen, Jingbo Zhou, Xiaojun Shan, Wenjie Du, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jiangbin Zheng, Stan Z. Li |
| 2024 | AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation. Teng Li, Liwen Zhang, Youcheng Zhang, ZijunHu, Pengcheng Pi, Zongqing Lu, Qingmin Liao, Zhe Ma |
| 2024 | AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making. Yizhe Huang, Xingbo Wang, Hao Liu, Fanqi Kong, Aoyang Qin, Min Tang, Xiaoxi Wang, Song-Chun Zhu, Mingjie Bi, Siyuan Qi, Xue Feng |
| 2024 | Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps. Benjamin Ellis, Matthew Thomas Jackson, Andrei Lupu, Alexander David Goldie, Mattie Fellows, Shimon Whiteson, Jakob N. Foerster |
| 2024 | Adam with model exponential moving average is effective for nonconvex optimization. Kwangjun Ahn, Ashok Cutkosky |
| 2024 | AdanCA: Neural Cellular Automata As Adaptors For More Robust Vision Transformer. Yitao Xu, Tong Zhang, Sabine Süsstrunk |
| 2024 | Adaptable Logical Control for Large Language Models. Honghua Zhang, Po-Nien Kung, Masahiro Yoshida, Guy Van den Broeck, Nanyun Peng |
| 2024 | Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis. Deepak Sridhar, Abhishek Peri, Rohith Rachala, Nuno Vasconcelos |
| 2024 | Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models. Gen Li, Yuling Yan |
| 2024 | Adaptive Depth Networks with Skippable Sub-Paths. Woochul Kang, Hyungseop Lee |
| 2024 | Adaptive Domain Learning for Cross-domain Image Denoising. Zian Qian, Chenyang Qi, Ka Lung Law, Hao Fu, Chenyang Lei, Qifeng Chen |
| 2024 | Adaptive Experimentation When You Can't Experiment. Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain |
| 2024 | Adaptive Exploration for Data-Efficient General Value Function Evaluations. Arushi Jain, Josiah Hanna, Doina Precup |
| 2024 | Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare. Hanwei Zhu, Haoning Wu, Yixuan Li, Zicheng Zhang, Baoliang Chen, Lingyu Zhu, Yuming Fang, Guangtao Zhai, Weisi Lin, Shiqi Wang |
| 2024 | Adaptive Important Region Selection with Reinforced Hierarchical Search for Dense Object Detection. Dingrong Wang, Hitesh Sapkota, Qi Yu |
| 2024 | Adaptive Labeling for Efficient Out-of-distribution Model Evaluation. Daksh Mittal, Yuanzhe Ma, Shalmali Joshi, Hongseok Namkoong |
| 2024 | Adaptive Layer Sparsity for Large Language Models via Activation Correlation Assessment. Wei Li, Lujun Li, Mark G. Lee, Shengjie Sun |
| 2024 | Adaptive Passive-Aggressive Framework for Online Regression with Side Information. Runhao Shi, Jiaxi Ying, Daniel P. Palomar |
| 2024 | Adaptive Preference Scaling for Reinforcement Learning with Human Feedback. Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao |
| 2024 | Adaptive Proximal Gradient Method for Convex Optimization. Yura Malitsky, Konstantin Mishchenko |
| 2024 | Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning. Jeonghye Kim, Suyoung Lee, Woojun Kim, Youngchul Sung |
| 2024 | Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences. Saiyue Lyu, Shadab Shaikh, Frederick Shpilevskiy, Evan Shelhamer, Mathias Lécuyer |
| 2024 | Adaptive Sampling for Efficient Softmax Approximation. Tavor Z. Baharav, Ryan Kang, Colin Sullivan, Mo Tiwari, Eric Luxenberg, David Tse, Mert Pilanci |
| 2024 | Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions. Wei Jiang, Sifan Yang, Yibo Wang, Lijun Zhang |
| 2024 | Adaptive Visual Scene Understanding: Incremental Scene Graph Generation. Naitik Khandelwal, Xiao Liu, Mengmi Zhang |
| 2024 | Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi, Aryan Mokhtari |
| 2024 | AdaptiveISP: Learning an Adaptive Image Signal Processor for Object Detection. Yujin Wang, Tianyi Xu, Zhang Fan, Tianfan Xue, Jinwei Gu |
| 2024 | Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation. Wenfang Yao, Chen Liu, Kejing Yin, William Kwok-Wai Cheung, Jing Qin |
| 2024 | Addressing Bias in Online Selection with Limited Budget of Comparisons. Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder, Vianney Perchet |
| 2024 | Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation. Yanghao Xiao, Haoxuan Li, Yongqiang Tang, Wensheng Zhang |
| 2024 | Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment. Jiawei Chen, Chunhui Zhao |
| 2024 | Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning. Ronglong Fang, Yuesheng Xu |
| 2024 | AdjointDEIS: Efficient Gradients for Diffusion Models. Zander W. Blasingame, Chen Liu |
| 2024 | Adjust Pearson's $r$ to Measure Arbitrary Monotone Dependence. Xinbo Ai |
| 2024 | AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks. Jin Li, Ziqiang He, Anwei Luo, Jian-Fang Hu, Z. Jane Wang, Xiangui Kang |
| 2024 | Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 - 15, 2024. Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, Cheng Zhang |
| 2024 | Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models. Yicheng Xu, Yuxin Chen, Jiahao Nie, Yusong Wang, Huiping Zhuang, Manabu Okumura |
| 2024 | Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation. Eyal Michaeli, Ohad Fried |
| 2024 | Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler. Kunyu Peng, Di Wen, Kailun Yang, Ao Luo, Yufan Chen, Jia Fu, M. Saquib Sarfraz, Alina Roitberg, Rainer Stiefelhagen |
| 2024 | Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators. Changze Lv, Dongqi Han, Yansen Wang, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li |
| 2024 | Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. Sijia Chen, Yibo Wang, Yi-Feng Wu, Qingguo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Lijun Zhang |
| 2024 | Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation. Chengting Yu, Lei Liu, Gaoang Wang, Erping Li, Aili Wang |
| 2024 | Advancing Video Anomaly Detection: A Concise Review and a New Dataset. Liyun Zhu, Lei Wang, Arjun Raj, Tom Gedeon, Chen Chen |
| 2024 | Advection Augmented Convolutional Neural Networks. Niloufar Zakariaei, Siddharth Rout, Eldad Haber, Moshe Eliasof |
| 2024 | Adversarial Environment Design via Regret-Guided Diffusion Models. Hojun Chung, Junseo Lee, Minsoo Kim, Dohyeong Kim, Songhwai Oh |
| 2024 | Adversarial Moment-Matching Distillation of Large Language Models. Chen Jia |
| 2024 | Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models. Yihao Zhang, Zeming Wei, Jun Sun, Meng Sun |
| 2024 | Adversarial Schrödinger Bridge Matching. Nikita Gushchin, Daniil Selikhanovych, Sergei Kholkin, Evgeny Burnaev, Alexander Korotin |
| 2024 | Adversarially Robust Decision Transformer. Xiaohang Tang, Afonso Marques, Parameswaran Kamalaruban, Ilija Bogunovic |
| 2024 | Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters. David P. Woodruff, Samson Zhou |
| 2024 | Adversarially Robust Multi-task Representation Learning. Austin Watkins, Thanh Nguyen-Tang, Enayat Ullah, Raman Arora |
| 2024 | Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning. Honghao Wei, Xiyue Peng, Arnob Ghosh, Xin Liu |
| 2024 | Agent Planning with World Knowledge Model. Shuofei Qiao, Runnan Fang, Ningyu Zhang, Yuqi Zhu, Xiang Chen, Shumin Deng, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen |
| 2024 | AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents. Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, Junxian He |
| 2024 | AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. Edoardo Debenedetti, Jie Zhang, Mislav Balunovic, Luca Beurer-Kellner, Marc Fischer, Florian Tramèr |
| 2024 | AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases. Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, Bo Li |
| 2024 | Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP. Chen Huang, Skyler Seto, Samira Abnar, David Grangier, Navdeep Jaitly, Joshua M. Susskind |
| 2024 | Aggregating Quantitative Relative Judgments: From Social Choice to Ranking Prediction. Yixuan Even Xu, Hanrui Zhang, Yu Cheng, Vincent Conitzer |
| 2024 | AirSketch: Generative Motion to Sketch. Hui Xian Grace Lim, Xuanming Cui, Yogesh S. Rawat, Ser Nam Lim |
| 2024 | AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data. Zifan Song, Yudong Wang, Wenwei Zhang, Kuikun Liu, Chengqi Lyu, Demin Song, Qipeng Guo, Hang Yan, Dahua Lin, Kai Chen, Cairong Zhao |
| 2024 | Algebraic Positional Encodings. Konstantinos Kogkalidis, Jean-Philippe Bernardy, Vikas Garg |
| 2024 | Algorithmic Capabilities of Random Transformers. Ziqian Zhong, Jacob Andreas |
| 2024 | Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists. Joachim Baumann, Celestine Mendler-Dünner |
| 2024 | Algorithmic progress in language models. Anson Ho, Tamay Besiroglu, Ege Erdil, Zifan Carl Guo, David Owen, Robi Rahman, David Atkinson, Neil Thompson, Jaime Sevilla |
| 2024 | Alias-Free Mamba Neural Operator. Jianwei Zheng, Wei Li, Ni Xu, Junwei Zhu, Xiaoxu Lin, Xiaoqin Zhang |
| 2024 | Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers. Adam Stooke, Rohit Prabhavalkar, Khe Chai Sim, Pedro Moreno Mengibar |
| 2024 | Aligner: Efficient Alignment by Learning to Correct. Jiaming Ji, Boyuan Chen, Hantao Lou, Donghai Hong, Borong Zhang, Xuehai Pan, Tianyi Qiu, Juntao Dai, Yaodong Yang |
| 2024 | Aligning Audio-Visual Joint Representations with an Agentic Workflow. Shentong Mo, Yibing Song |
| 2024 | Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control. Huayu Chen, Kaiwen Zheng, Hang Su, Jun Zhu |
| 2024 | Aligning Diffusion Models by Optimizing Human Utility. Shufan Li, Konstantinos Kallidromitis, Akash Gokul, Yusuke Kato, Kazuki Kozuka |
| 2024 | Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem. Mathieu Even, Luca Ganassali, Jakob Maier, Laurent Massoulié |
| 2024 | Aligning Individual and Collective Objectives in Multi-Agent Cooperation. Yang Li, Wenhao Zhang, Jianhong Wang, Shao Zhang, Yali Du, Ying Wen, Wei Pan |
| 2024 | Aligning LLM Agents by Learning Latent Preference from User Edits. Ge Gao, Alexey Taymanov, Eduardo Salinas, Paul Mineiro, Dipendra Misra |
| 2024 | Aligning Large Language Models with Representation Editing: A Control Perspective. Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang |
| 2024 | Aligning Model Properties via Conformal Risk Control. William Overman, Jacqueline Jil Vallon, Mohsen Bayati |
| 2024 | Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization. Siyi Gu, Minkai Xu, Alexander S. Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon |
| 2024 | Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms. Miaosen Zhang, Yixuan Wei, Zhen Xing, Yifei Ma, Zuxuan Wu, Ji Li, Zheng Zhang, Qi Dai, Chong Luo, Xin Geng, Baining Guo |
| 2024 | Aligning to Thousands of Preferences via System Message Generalization. Seongyun Lee, Sue Hyun Park, Seungone Kim, Minjoon Seo |
| 2024 | Alignment at Pre-training! Towards Native Alignment for Arabic LLMs. Juhao Liang, Zhenyang Cai, Jianqing Zhu, Huang Huang, Kewei Zong, Bang An, Mosen Alharthi, Juncai He, Lian Zhang, Haizhou Li, Benyou Wang, Jinchao Xu |
| 2024 | Alignment for Honesty. Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, Pengfei Liu |
| 2024 | All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation. Xu Zhang, Peiyao Guo, Ming Lu, Zhan Ma |
| 2024 | AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery. Hangyu Zhou, Chia-Hsiang Kao, Cheng Perng Phoo, Utkarsh Mall, Bharath Hariharan, Kavita Bala |
| 2024 | Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering. Suyuan Liu, Siwei Wang, Ke Liang, Junpu Zhang, Zhibin Dong, Tianrui Liu, En Zhu, Xinwang Liu, Kunlun He |
| 2024 | Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization. Qihao Liu, Zhanpeng Zeng, Ju He, Qihang Yu, Xiaohui Shen, Liang-Chieh Chen |
| 2024 | Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization. Xinyu Lyu, Beitao Chen, Lianli Gao, Hengtao Shen, Jingkuan Song |
| 2024 | Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits. Shuai Liu, Alex Ayoub, Flore Sentenac, Xiaoqi Tan, Csaba Szepesvári |
| 2024 | Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits. Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong |
| 2024 | Almost Surely Asymptotically Constant Graph Neural Networks. Sam Adam-Day, Michael Benedikt, Ismail Ilkan Ceylan, Ben Finkelshtein |
| 2024 | Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction. Manuel Brenner, Christoph Jürgen Hemmer, Zahra Monfared, Daniel Durstewitz |
| 2024 | AlphaMath Almost Zero: Process Supervision without Process. Guoxin Chen, Minpeng Liao, Chengxi Li, Kai Fan |
| 2024 | AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models. Haiquan Lu, Yefan Zhou, Shiwei Liu, Zhangyang Wang, Michael W. Mahoney, Yaoqing Yang |
| 2024 | AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos. Yuze He, Wang Zhao, Shaohui Liu, Yubin Hu, Yushi Bai, Yu-Hui Wen, Yongjin Liu |
| 2024 | AlterMOMA: Fusion Redundancy Pruning for Camera-LiDAR Fusion Models with Alternative Modality Masking. Shiqi Sun, Yantao Lu, Ning Liu, Bo Jiang, Jinchao Chen, Ying Zhang |
| 2024 | Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image Classification and Object Detection. Dongsu Song, Daehwa Ko, Jay Hoon Jung |
| 2024 | AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment. Yonggan Fu, Zhongzhi Yu, Junwei Li, Jiayi Qian, Yongan Zhang, Xiangchi Yuan, Dachuan Shi, Roman Yakunin, Yingyan (Celine) Lin |
| 2024 | Amortized Active Causal Induction with Deep Reinforcement Learning. Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster |
| 2024 | Amortized Bayesian Experimental Design for Decision-Making. Daolang Huang, Yujia Guo, Luigi Acerbi, Samuel Kaski |
| 2024 | Amortized Eigendecomposition for Neural Networks. Tianbo Li, Zekun Shi, Jiaxi Zhao, Min Lin |
| 2024 | Amortized Fourier Neural Operators. Zipeng Xiao, Siqi Kou, Zhongkai Hao, Bokai Lin, Zhijie Deng |
| 2024 | Amortized Planning with Large-Scale Transformers: A Case Study on Chess. Anian Ruoss, Grégoire Delétang, Sourabh Medapati, Jordi Grau-Moya, Kevin Li, Elliot Catt, John Reid, Cannada A. Lewis, Joel Veness, Tim Genewein |
| 2024 | Amortizing intractable inference in diffusion models for vision, language, and control. Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Nikolay Malkin |
| 2024 | An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness. Xiaochuan Gong, Jie Hao, Mingrui Liu |
| 2024 | An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization. Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani, Aryan Mokhtari |
| 2024 | An Adaptive Approach for Infinitely Many-armed Bandits under Generalized Rotting Constraints. Jung-Hun Kim, Milan Vojnovic, Se-Young Yun |
| 2024 | An Analysis of Elo Rating Systems via Markov Chains. Sam Olesker-Taylor, Luca Zanetti |
| 2024 | An Analysis of Tokenization: Transformers under Markov Data. Nived Rajaraman, Jiantao Jiao, Kannan Ramchandran |
| 2024 | An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning. Manel Rodriguez-Soto, Juan A. Rodríguez-Aguilar, Maite López-Sánchez |
| 2024 | An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling. Shenbao Yu, Yinghui Pan, Yifeng Zeng, Prashant Doshi, Guoquan Liu, Kim-Leng Poh, Mingwei Lin |
| 2024 | An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations. Shengbo Wang, Jose H. Blanchet, Peter W. Glynn |
| 2024 | An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning. Dong Li, Aijia Zhang, Junqi Gao, Biqing Qi |
| 2024 | An Efficient Recipe for Long Context Extension via Middle-Focused Positional Encoding. Tong Wu, Yanpeng Zhao, Zilong Zheng |
| 2024 | An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval. Huilong Jin, Yingxue Zhang, Lei Shi, Shuang Zhang, Feifei Kou, Jiapeng Yang, Chuangying Zhu, Jia Luo |
| 2024 | An Equivalence Between Static and Dynamic Regret Minimization. Andrew Jacobsen, Francesco Orabona |
| 2024 | An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations. Weimin Bai, Yifei Wang, Wenzheng Chen, He Sun |
| 2024 | An Image is Worth 32 Tokens for Reconstruction and Generation. Qihang Yu, Mark Weber, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen |
| 2024 | An Improved Empirical Fisher Approximation for Natural Gradient Descent. Xiaodong Wu, Wenyi Yu, Chao Zhang, Philip C. Woodland |
| 2024 | An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models. Yunzhe Hu, Difan Zou, Dong Xu |
| 2024 | An Information Theoretic Perspective on Conformal Prediction. Alvaro H. C. Correia, Fabio Valerio Massoli, Christos Louizos, Arash Behboodi |
| 2024 | An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning. Qian Lin, Zongkai Liu, Danying Mo, Chao Yu |
| 2024 | An effective framework for estimating individualized treatment rules. Joowon Lee, Jared D. Huling, Guanhua Chen |
| 2024 | An engine not a camera: Measuring performative power of online search. Celestine Mendler-Dünner, Gabriele Carovano, Moritz Hardt |
| 2024 | An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem. Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Chris Mingard, Ard A. Louis |
| 2024 | An eye for an ear: zero-shot audio description leveraging an image captioner with audio-visual token distribution matching. Hugo Malard, Michel Olvera, Stéphane Lathuilière, Slim Essid |
| 2024 | Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko |
| 2024 | Analysing the Generalisation and Reliability of Steering Vectors. Daniel Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adrià Garriga-Alonso, Robert Kirk |
| 2024 | Analysis of Corrected Graph Convolutions. Robert Wang, Aseem Baranwal, Kimon Fountoulakis |
| 2024 | Analytically deriving Partial Information Decomposition for affine systems of stable and convolution-closed distributions. Chaitanya Goswami, Amanda Merkley |
| 2024 | Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training. Atli Kosson, Bettina Messmer, Martin Jaggi |
| 2024 | Animal-Bench: Benchmarking Multimodal Video Models for Animal-centric Video Understanding. Yinuo Jing, Ruxu Zhang, Kongming Liang, Yongxiang Li, Zhongjiang He, Zhanyu Ma, Jun Guo |
| 2024 | Animate3D: Animating Any 3D Model with Multi-view Video Diffusion. Yanqin Jiang, Chaohui Yu, Chenjie Cao, Fan Wang, Weiming Hu, Jin Gao |
| 2024 | Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing. David Perera, Victor Letzelter, Théo Mariotte, Adrien Cortés, Mickaël Chen, Slim Essid, Gaël Richard |
| 2024 | Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization. Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, Quanquan Gu |
| 2024 | Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss. Paul Krzakala, Junjie Yang, Rémi Flamary, Florence d'Alché-Buc, Charlotte Laclau, Matthieu Labeau |
| 2024 | Any2Policy: Learning Visuomotor Policy with Any-Modality. Yichen Zhu, Zhicai Ou, Feifei Feng, Jian Tang |
| 2024 | AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario. Yuhan Li, Hao Zhou, Wenxiang Shang, Ran Lin, Xuanhong Chen, Bingbing Ni |
| 2024 | Apathetic or Empathetic? Evaluating LLMs' Emotional Alignments with Humans. Jen-tse Huang, Man Ho Lam, Eric John Li, Shujie Ren, Wenxuan Wang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu |
| 2024 | Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models. Tuomas Kynkäänniemi, Miika Aittala, Tero Karras, Samuli Laine, Timo Aila, Jaakko Lehtinen |
| 2024 | Approaching Human-Level Forecasting with Language Models. Danny Halawi, Fred Zhang, Chen Yueh-Han, Jacob Steinhardt |
| 2024 | Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient. Shaoqi Wang, Chunjie Yang, Siwei Lou |
| 2024 | Approximately Equivariant Neural Processes. Matthew Ashman, Cristiana Diaconu, Adrian Weller, Wessel P. Bruinsma, Richard E. Turner |
| 2024 | Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering. Anna Arutyunova, Jan Eube, Heiko Röglin, Melanie Schmidt, Sarah Sturm, Julian Wargalla |
| 2024 | Approximating mutual information of high-dimensional variables using learned representations. Gokul Gowri, Xiao-Kang Lun, Allon M. Klein, Peng Yin |
| 2024 | Approximating the Top Eigenvector in Random Order Streams. Praneeth Kacham, David P. Woodruff |
| 2024 | Approximation Rate of the Transformer Architecture for Sequence Modeling. Haotian Jiang, Qianxiao Li |
| 2024 | Approximation-Aware Bayesian Optimization. Natalie Maus, Kyurae Kim, David Eriksson, Geoff Pleiss, John P. Cunningham, Jacob R. Gardner |
| 2024 | Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era. Yohann Perron, Vladyslav Sydorov, Adam P. Wijker, Damian Evans, Christophe Pottier, Loïc Landrieu |
| 2024 | Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting. Yian Wang, Xiaowen Qiu, Jiageng Liu, Zhehuan Chen, Jiting Cai, Yufei Wang, Tsun-Hsuan Johnson Wang, Zhou Xian, Chuang Gan |
| 2024 | Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification. Jannik Franzen, Claudia Winklmayr, Vanessa Emanuela Guarino, Christoph Karg, Xiaoyan Yu, Nora Koreuber, Jan Philipp Albrecht, Philip Bischoff, Dagmar Kainmueller |
| 2024 | Are Graph Neural Networks Optimal Approximation Algorithms? Morris Yau, Nikolaos Karalias, Eric Lu, Jessica Xu, Stefanie Jegelka |
| 2024 | Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks? Jiacheng Cen, Anyi Li, Ning Lin, Yuxiang Ren, Zihe Wang, Wenbing Huang |
| 2024 | Are Language Models Actually Useful for Time Series Forecasting? Mingtian Tan, Mike A. Merrill, Vinayak Gupta, Tim Althoff, Tom Hartvigsen |
| 2024 | Are Large Language Models Good Statisticians? Yizhang Zhu, Shiyin Du, Boyan Li, Yuyu Luo, Nan Tang |
| 2024 | Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation? Lingao Xiao, Yang He |
| 2024 | Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems. Lingjiao Chen, Jared Quincy Davis, Boris Hanin, Peter Bailis, Ion Stoica, Matei A. Zaharia, James Y. Zou |
| 2024 | Are Multiple Instance Learning Algorithms Learnable for Instances? Jaeseok Jang, Hyuk-Yoon Kwon |
| 2024 | Are Self-Attentions Effective for Time Series Forecasting? Dongbin Kim, Jinseong Park, Jaewook Lee, Hoki Kim |
| 2024 | Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? Maohao Shen, Jongha Jon Ryu, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, Gregory W. Wornell |
| 2024 | Are We on the Right Way for Evaluating Large Vision-Language Models? Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, Feng Zhao |
| 2024 | Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections. Zihan Luo, Hong Huang, Yongkang Zhou, Jiping Zhang, Nuo Chen, Hai Jin |
| 2024 | Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology. Dhananjay Tomar, Alexander Binder, Andreas Kleppe |
| 2024 | ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction. Renze Chen, Zhuofeng Wang, Beiquan Cao, Tong Wu, Size Zheng, Xiuhong Li, Xuechao Wei, Shengen Yan, Meng Li, Yun Liang |
| 2024 | Artemis: Towards Referential Understanding in Complex Videos. Jihao Qiu, Yuan Zhang, Xi Tang, Lingxi Xie, TianRen Ma, Pengyu Yan, David S. Doermann, Qixiang Ye, Yunjie Tian |
| 2024 | Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis. Jianning Deng, Kartic Subr, Hakan Bilen |
| 2024 | Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning. Jonathan Cook, Chris Lu, Edward Hughes, Joel Z. Leibo, Jakob N. Foerster |
| 2024 | AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation. Anil Kag, Huseyin Coskun, Jierun Chen, Junli Cao, Willi Menapace, Aliaksandr Siarohin, Sergey Tulyakov, Jian Ren |
| 2024 | AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction. Chunan Liu, Lilian Denzler, Yihong Chen, Andrew C. R. Martin, Brooks Paige |
| 2024 | Ask, Attend, Attack: An Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models. Qingyuan Zeng, Zhenzhong Wang, Yiu-ming Cheung, Min Jiang |
| 2024 | Assemblage: Automatic Binary Dataset Construction for Machine Learning. Chang Liu, Rebecca Saul, Yihao Sun, Edward Raff, Maya Fuchs, Townsend Southard Pantano, James Holt, Kristopher K. Micinski |
| 2024 | Assembly Fuzzy Representation on Hypergraph for Open-Set 3D Object Retrieval. Yang Xu, Yifan Feng, Jun Zhang, Jun-Hai Yong, Yue Gao |
| 2024 | Association Pattern-aware Fusion for Biological Entity Relationship Prediction. Lingxiang Jia, Yuchen Ying, Zunlei Feng, Zipeng Zhong, Shaolun Yao, Jiacong Hu, Mingjiang Duan, Xingen Wang, Jie Song, Mingli Song |
| 2024 | Association of Objects May Engender Stereotypes: Mitigating Association-Engendered Stereotypes in Text-to-Image Generation. Junlei Zhou, Jiashi Gao, Xiangyu Zhao, Xin Yao, Xuetao Wei |
| 2024 | Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability. Fan Chen, Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin, Yunbei Xu |
| 2024 | Asymptotics of Alpha-Divergence Variational Inference Algorithms with Exponential Families. François Bertholom, Randal Douc, François Roueff |
| 2024 | AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising. Zigeng Chen, Xinyin Ma, Gongfan Fang, Zhenxiong Tan, Xinchao Wang |
| 2024 | Asynchronous Perception Machine for Efficient Test Time Training. Rajat Modi, Yogesh S. Rawat |
| 2024 | Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication. Yunuo Chen, Tianyi Xie, Zeshun Zong, Xuan Li, Feng Gao, Yin Yang, Ying Nian Wu, Chenfanfu Jiang |
| 2024 | Attack-Aware Noise Calibration for Differential Privacy. Bogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Flávio P. Calmon, Carmela Troncoso |
| 2024 | Attack-Resilient Image Watermarking Using Stable Diffusion. Lijun Zhang, Xiao Liu, Antoni Viros Martin, Cindy Xiong Bearfield, Yuriy Brun, Hui Guan |
| 2024 | Attention Temperature Matters in ViT-Based Cross-Domain Few-Shot Learning. Yixiong Zou, Ran Ma, Yuhua Li, Ruixuan Li |
| 2024 | Attention boosted Individualized Regression. Guang Yang, Yuan Cao, Long Feng |
| 2024 | AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation. Lianyu Pang, Jian Yin, Baoquan Zhao, Feize Wu, Fu Lee Wang, Qing Li, Xudong Mao |
| 2024 | Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective. Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang |
| 2024 | AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games. Kefan Su, Yusen Huo, Zhilin Zhang, Shuai Dou, Chuan Yu, Jian Xu, Zongqing Lu, Bo Zheng |
| 2024 | AudioMarkBench: Benchmarking Robustness of Audio Watermarking. Hongbin Liu, Moyang Guo, Zhengyuan Jiang, Lun Wang, Neil Gong |
| 2024 | Auditing Local Explanations is Hard. Robi Bhattacharjee, Ulrike von Luxburg |
| 2024 | Auditing Privacy Mechanisms via Label Inference Attacks. Róbert Busa-Fekete, Travis Dick, Claudio Gentile, Andrés Muñoz Medina, Adam Smith, Marika Swanberg |
| 2024 | AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents. Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, Honglak Lee |
| 2024 | AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning. Minghao Chen, Yihang Li, Yanting Yang, Shiyu Yu, Binbin Lin, Xiaofei He |
| 2024 | AutoMix: Automatically Mixing Language Models. Pranjal Aggarwal, Aman Madaan, Ankit Anand, Srividya Pranavi Potharaju, Swaroop Mishra, Pei Zhou, Aditya Gupta, Dheeraj Rajagopal, Karthik Kappaganthu, Yiming Yang, Shyam Upadhyay, Manaal Faruqui, Mausam |
| 2024 | AutoPSV: Automated Process-Supervised Verifier. Jianqiao Lu, Zhiyang Dou, Hongru Wang, Zeyu Cao, Jianbo Dai, Yunlong Feng, Zhijiang Guo |
| 2024 | AutoSurvey: Large Language Models Can Automatically Write Surveys. Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, Xin Zhang, Zhen Wu, Meishan Zhang, Xinyu Dai, Min Zhang, Qingsong Wen, Wei Ye, Shikun Zhang, Yue Zhang |
| 2024 | AutoTimes: Autoregressive Time Series Forecasters via Large Language Models. Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long |
| 2024 | Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency. Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo |
| 2024 | Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency. Zenan Li, Yifan Wu, Zhaoyu Li, Xinming Wei, Xian Zhang, Fan Yang, Xiaoxing Ma |
| 2024 | Automated Efficient Estimation using Monte Carlo Efficient Influence Functions. Raj Agrawal, Sam Witty, Andy Zane, Elias Bingham |
| 2024 | Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs. Rong Ma, Jie Chen, Xiangyang Xue, Jian Pu |
| 2024 | Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records. Suhan Cui, Prasenjit Mitra |
| 2024 | Automated Multi-level Preference for MLLMs. Mengxi Zhang, Wenhao Wu, Yu Lu, Yuxin Song, Kang Rong, Huanjin Yao, Jianbo Zhao, Fanglong Liu, Haocheng Feng, Jingdong Wang, Yifan Sun |
| 2024 | Automatic Outlier Rectification via Optimal Transport. Jose H. Blanchet, Jiajin Li, Markus Pelger, Greg Zanotti |
| 2024 | Automatically Learning Hybrid Digital Twins of Dynamical Systems. Samuel Holt, Tennison Liu, Mihaela van der Schaar |
| 2024 | Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions. Tian Xie, Xueru Zhang |
| 2024 | Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models. Jiahao Ying, Yixin Cao, Yushi Bai, Qianru Sun, Bo Wang, Wei Tang, Zhaojun Ding, Yizhe Yang, Xuanjing Huang, Shuicheng Yan |
| 2024 | Autonomous Agents for Collaborative Task under Information Asymmetry. Wei Liu, Chenxi Wang, Yifei Wang, Zihao Xie, Rennai Qiu, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Chen Qian |
| 2024 | Autonomous Driving with Spiking Neural Networks. Ruijie Zhu, Ziqing Wang, Leilani Gilpin, Jason Eshraghian |
| 2024 | Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI. Guanxiong Luo, Shoujin Huang, Martin Uecker |
| 2024 | Autoregressive Image Generation without Vector Quantization. Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He |
| 2024 | Autoregressive Policy Optimization for Constrained Allocation Tasks. David Winkel, Niklas Strauß, Maximilian Bernhard, Zongyue Li, Thomas Seidl, Matthias Schubert |
| 2024 | AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning. Shirley Wu, Shiyu Zhao, Qian Huang, Kexin Huang, Michihiro Yasunaga, Kaidi Cao, Vassilis N. Ioannidis, Karthik Subbian, Jure Leskovec, James Y. Zou |
| 2024 | AverNet: All-in-one Video Restoration for Time-varying Unknown Degradations. Haiyu Zhao, Lei Tian, Xinyan Xiao, Peng Hu, Yuanbiao Gou, Xi Peng |
| 2024 | Average gradient outer product as a mechanism for deep neural collapse. Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin |
| 2024 | Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments. Wen-Bo Du, Tian Qin, Tian-Zuo Wang, Zhi-Hua Zhou |
| 2024 | Axioms for AI Alignment from Human Feedback. Luise Ge, Daniel Halpern, Evi Micha, Ariel D. Procaccia, Itai Shapira, Yevgeniy Vorobeychik, Junlin Wu |
| 2024 | B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory. Luca Zancato, Arjun Seshadri, Yonatan Dukler, Aditya Golatkar, Yantao Shen, Benjamin Bowman, Matthew Trager, Alessandro Achille, Stefano Soatto |
| 2024 | B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data. Runze You, Shi Pu |
| 2024 | B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable. Shreyash Arya, Sukrut Rao, Moritz Böhle, Bernt Schiele |
| 2024 | BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack. Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Ivan Rodkin, Dmitry Sorokin, Artyom Y. Sorokin, Mikhail Burtsev |
| 2024 | BAKU: An Efficient Transformer for Multi-Task Policy Learning. Siddhant Haldar, Zhuoran Peng, Lerrel Pinto |
| 2024 | BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts. Qizhen (Irene) Zhang, Nikolas Gritsch, Dwaraknath Gnaneshwar, Simon Guo, David Cairuz, Bharat Venkitesh, Jakob N. Foerster, Phil Blunsom, Sebastian Ruder, Ahmet Üstün, Acyr Locatelli |
| 2024 | BAN: Detecting Backdoors Activated by Adversarial Neuron Noise. Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, Shujian Yu, Stjepan Picek |
| 2024 | BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models. Qijun Luo, Hengxu Yu, Xiao Li |
| 2024 | BEACON: Benchmark for Comprehensive RNA Tasks and Language Models. Yuchen Ren, Zhiyuan Chen, Lifeng Qiao, Hongtai Jing, Yuchen Cai, Sheng Xu, Peng Ye, Xinzhu Ma, Siqi Sun, Hongliang Yan, Dong Yuan, Wanli Ouyang, Xihui Liu |
| 2024 | BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning. Haohong Lin, Wenhao Ding, Jian Chen, Laixi Shi, Jiacheng Zhu, Bo Li, Ding Zhao |
| 2024 | BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models. Fangyikang Wang, Hubery Yin, Yuejiang Dong, Huminhao Zhu, Zhang Chao, Hanbin Zhao, Hui Qian, Chen Li |
| 2024 | BERTs are Generative In-Context Learners. David Samuel |
| 2024 | BIGOS V2 Benchmark for Polish ASR: Curated Datasets and Tools for Reproducible Evaluation. Michal Junczyk |
| 2024 | BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity. Zahra Gharaee, Scott C. Lowe, ZeMing Gong, Pablo Millan Arias, Nicholas Pellegrino, Austin T. Wang, Joakim Bruslund Haurum, Iuliia Zarubiieva, Lila Kari, Dirk Steinke, Graham W. Taylor, Paul W. Fieguth, Angel X. Chang |
| 2024 | BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference. Changwoo Lee, Soo Min Kwon, Qing Qu, Hun-Seok Kim |
| 2024 | BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages. Junho Myung, Nayeon Lee, Yi Zhou, Jiho Jin, Rifki Afina Putri, Dimosthenis Antypas, Hsuvas Borkakoty, Eunsu Kim, Carla Pérez-Almendros, Abinew Ali Ayele, Víctor Gutiérrez-Basulto, Yazmín Ibáñez-García, Hwaran Lee, Shamsuddeen Hassan Muhammad, Ki-Woong Park, Anar Rzayev, Nina White, Seid Muhie Yimam, Mohammad Taher Pilehvar, Nedjma Ousidhoum, José Camacho-Collados, Alice Oh |
| 2024 | BLURD: Benchmarking and Learning using a Unified Rendering and Diffusion Model. Boris Repasky, Ehsan Abbasnejad, Anthony R. Dick |
| 2024 | BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models. Yibin Wang, Haizhou Shi, Ligong Han, Dimitris N. Metaxas, Hao Wang |
| 2024 | BMRS: Bayesian Model Reduction for Structured Pruning. Dustin Wright, Christian Igel, Raghavendra Selvan |
| 2024 | BOLD: Boolean Logic Deep Learning. Van Minh Nguyen, Cristian Ocampo-Blandon, Aymen Askri, Louis Leconte, Ba-Hien Tran |
| 2024 | BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning. Jianming Pan, Zeqi Ye, Xiao Yang, Xu Yang, Weiqing Liu, Lewen Wang, Jiang Bian |
| 2024 | Back to the Continuous Attractor. Ábel Ságodi, Guillermo Martín-Sánchez, Piotr A. Sokól, Memming Park |
| 2024 | BackTime: Backdoor Attacks on Multivariate Time Series Forecasting. Xiao Lin, Zhining Liu, Dongqi Fu, Ruizhong Qiu, Hanghang Tong |
| 2024 | BackdoorAlign: Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced Safety Alignment. Jiongxiao Wang, Jiazhao Li, Yiquan Li, Xiangyu Qi, Junjie Hu, Sharon Li, Patrick McDaniel, Muhao Chen, Bo Li, Chaowei Xiao |
| 2024 | Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Zhao Xu, Fan Liu, Hao Liu |
| 2024 | Balancing Context Length and Mixing Times for Reinforcement Learning at Scale. Matthew Riemer, Khimya Khetarpal, Janarthanan Rajendran, Sarath Chandar |
| 2024 | Banded Square Root Matrix Factorization for Differentially Private Model Training. Nikita P. Kalinin, Christoph H. Lampert |
| 2024 | Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs. Yuval Filmus, Steve Hanneke, Idan Mehalel, Shay Moran |
| 2024 | Bandits with Abstention under Expert Advice. Stephen Pasteris, Alberto Rumi, Maximilian Thiessen, Shota Saito, Atsushi Miyauchi, Fabio Vitale, Mark Herbster |
| 2024 | Bandits with Preference Feedback: A Stackelberg Game Perspective. Barna Pásztor, Parnian Kassraie, Andreas Krause |
| 2024 | Bandits with Ranking Feedback. Davide Maran, Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Castiglioni, Nicola Gatti, Marcello Restelli |
| 2024 | Barely Random Algorithms and Collective Metrical Task Systems. Romain Cosson, Laurent Massoulié |
| 2024 | Base of RoPE Bounds Context Length. Mingyu Xu, Xin Men, Bingning Wang, Qingyu Zhang, Hongyu Lin, Xianpei Han, Weipeng Chen |
| 2024 | Batched Energy-Entropy acquisition for Bayesian Optimization. Felix Teufel, Carsten Stahlhut, Jesper Ferkinghoff-Borg |
| 2024 | Bayes-optimal learning of an extensive-width neural network from quadratically many samples. Antoine Maillard, Emanuele Troiani, Simon Martin, Florent Krzakala, Lenka Zdeborová |
| 2024 | Bayesian Adaptive Calibration and Optimal Design. Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla |
| 2024 | Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing. Yanfang Ling, Jiyong Li, Lingbo Li, Shangsong Liang |
| 2024 | Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization. Nicola Bariletto, Nhat Ho |
| 2024 | Bayesian Online Natural Gradient (BONG). Matt Jones, Peter G. Chang, Kevin P. Murphy |
| 2024 | Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal. Juliusz Ziomek, Masaki Adachi, Michael A. Osborne |
| 2024 | Bayesian Optimization of Functions over Node Subsets in Graphs. Huidong Liang, Xingchen Wan, Xiaowen Dong |
| 2024 | Bayesian Strategic Classification. Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani |
| 2024 | Bayesian-guided Label Mapping for Visual Reprogramming. Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, Feng Liu |
| 2024 | Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models. Deep Shankar Pandey, Spandan Pyakurel, Qi Yu |
| 2024 | Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs. Abhimanyu Hans, John Kirchenbauer, Yuxin Wen, Neel Jain, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein |
| 2024 | BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text. Siyan Wang, Bradford Levy |
| 2024 | Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback. Haolin Liu, Zakaria Mhammedi, Chen-Yu Wei, Julian Zimmert |
| 2024 | BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction. Zikang Zhou, Haibo Hu, Xinhong Chen, Jianping Wang, Nan Guan, Kui Wu, Yung-Hui Li, Yu-Kai Huang, Chun Jason Xue |
| 2024 | Belief-State Query Policies for User-Aligned POMDPs. Daniel Bramblett, Siddharth Srivastava |
| 2024 | Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving. Xiaosong Jia, Zhenjie Yang, Qifeng Li, Zhiyuan Zhang, Junchi Yan |
| 2024 | BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays. Yang Zhou, Tan Li Hui Faith, Yanyu Xu, Sicong Leng, Xinxing Xu, Yong Liu, Rick Siow Mong Goh |
| 2024 | Benchmark Data Repositories for Better Benchmarking. Rachel Longjohn, Markelle Kelly, Sameer Singh, Padhraic Smyth |
| 2024 | Benchmarking Complex Instruction-Following with Multiple Constraints Composition. Bosi Wen, Pei Ke, Xiaotao Gu, Lindong Wu, Hao Huang, Jinfeng Zhou, Wenchuang Li, Binxin Hu, Wendy Gao, Jiaxing Xu, Yiming Liu, Jie Tang, Hongning Wang, Minlie Huang |
| 2024 | Benchmarking Counterfactual Image Generation. Thomas Melistas, Nikos Spyrou, Nefeli Gkouti, Pedro Sanchez, Athanasios Vlontzos, Yannis Panagakis, Giorgos Papanastasiou, Sotirios A. Tsaftaris |
| 2024 | Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm. R. Teal Witter, Christopher Musco |
| 2024 | Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming. Victor-Alexandru Padurean, Adish Singla |
| 2024 | Benchmarking LLMs via Uncertainty Quantification. Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F. Wong, Emine Yilmaz, Shuming Shi, Zhaopeng Tu |
| 2024 | Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. Spandan Madan, Will Xiao, Mingran Cao, Hanspeter Pfister, Margaret S. Livingstone, Gabriel Kreiman |
| 2024 | Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime. Haoyu Geng, Hang Ruan, Runzhong Wang, Yang Li, Yang Wang, Lei Chen, Junchi Yan |
| 2024 | Benchmarking Structural Inference Methods for Interacting Dynamical Systems with Synthetic Data. Aoran Wang, Tsz Pan Tong, Andrzej Mizera, Jun Pang |
| 2024 | Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks. Bálint Mucsányi, Michael Kirchhof, Seong Joon Oh |
| 2024 | Benchmarking the Attribution Quality of Vision Models. Robin Hesse, Simone Schaub-Meyer, Stefan Roth |
| 2024 | BendVLM: Test-Time Debiasing of Vision-Language Embeddings. Walter Gerych, Haoran Zhang, Kimia Hamidieh, Eileen Pan, Maanas K. Sharma, Tom Hartvigsen, Marzyeh Ghassemi |
| 2024 | Benign overfitting in leaky ReLU networks with moderate input dimension. Kedar Karhadkar, Erin George, Michael Murray, Guido F. Montúfar, Deanna Needell |
| 2024 | BertaQA: How Much Do Language Models Know About Local Culture? Julen Etxaniz, Gorka Azkune, Aitor Soroa, Oier Lopez de Lacalle, Mikel Artetxe |
| 2024 | Better by default: Strong pre-tuned MLPs and boosted trees on tabular data. David Holzmüller, Léo Grinsztajn, Ingo Steinwart |
| 2024 | BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices. Anka Reuel-Lamparth, Amelia F. Hardy, Chandler Smith, Max Lamparth, Malcolm Hardy, Mykel J. Kochenderfer |
| 2024 | BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation. Xiang Zhang, Bingxin Ke, Hayko Riemenschneider, Nando Metzger, Anton Obukhov, Markus Gross, Konrad Schindler, Christopher Schroers |
| 2024 | Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation. Hangcheng Liu, Zhenhu Wu, Hao Wang, Xingshuo Han, Shangwei Guo, Tao Xiang, Tianwei Zhang |
| 2024 | Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales. Tang Li, Mengmeng Ma, Xi Peng |
| 2024 | Beyond Accuracy: Tracking more like Human via Visual Search. Dailing Zhang, Shiyu Hu, Xiaokun Feng, Xuchen Li, Meiqi Wu, Jing Zhang, Kaiqi Huang |
| 2024 | Beyond Aesthetics: Cultural Competence in Text-to-Image Models. Nithish Kannen, Arif Ahmad, Marco Andreetto, Vinodkumar Prabhakaran, Utsav Prabhu, Adji Bousso Dieng, Pushpak Bhattacharyya, Shachi Dave |
| 2024 | Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? Sonia Laguna, Ricards Marcinkevics, Moritz Vandenhirtz, Julia E. Vogt |
| 2024 | Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization. Dingshuo Chen, Zhixun Li, Yuyan Ni, Guibin Zhang, Ding Wang, Qiang Liu, Shu Wu, Jeffrey Xu Yu, Liang Wang |
| 2024 | Beyond Euclidean: Dual-Space Representation Learning for Weakly Supervised Video Violence Detection. Jiaxu Leng, Zhanjie Wu, Mingpi Tan, Yiran Liu, Ji Gan, Haosheng Chen, Xinbo Gao |
| 2024 | Beyond Optimism: Exploration With Partially Observable Rewards. Simone Parisi, Alireza Kazemipour, Michael Bowling |
| 2024 | Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints. Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Federico Fusco |
| 2024 | Beyond Prompts: Dynamic Conversational Benchmarking of Large Language Models. David Castillo-Bolado, Joseph Davidson, Finlay Gray, Marek Rosa |
| 2024 | Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning. Zhixiang Shen, Shuo Wang, Zhao Kang |
| 2024 | Beyond Single Stationary Policies: Meta-Task Players as Naturally Superior Collaborators. Haoming Wang, Zhaoming Tian, Yunpeng Song, Xiangliang Zhang, Zhongmin Cai |
| 2024 | Beyond Slow Signs in High-fidelity Model Extraction. Hanna Foerster, Robert Mullins, Ilia Shumailov, Jamie Hayes |
| 2024 | Beyond task diversity: provable representation transfer for sequential multitask linear bandits. Thang Duong, Zhi Wang, Chicheng Zhang |
| 2024 | Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects. Michael A. Lepori, Alexa R. Tartaglini, Wai Keen Vong, Thomas Serre, Brenden M. Lake, Ellie Pavlick |
| 2024 | BiDM: Pushing the Limit of Quantization for Diffusion Models. Xingyu Zheng, Xianglong Liu, Yichen Bian, Xudong Ma, Yulun Zhang, Jiakai Wang, Jinyang Guo, Haotong Qin |
| 2024 | BiScope: AI-generated Text Detection by Checking Memorization of Preceding Tokens. Hanxi Guo, Siyuan Cheng, Xiaolong Jin, Zhuo Zhang, Kaiyuan Zhang, Guanhong Tao, Guangyu Shen, Xiangyu Zhang |
| 2024 | BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval. Imanol Miranda, Ander Salaberria, Eneko Agirre, Gorka Azkune |
| 2024 | Bias Amplification in Language Model Evolution: An Iterated Learning Perspective. Yi Ren, Shangmin Guo, Linlu Qiu, Bailin Wang, Danica J. Sutherland |
| 2024 | Bias Detection via Signaling. Yiling Chen, Tao Lin, Ariel D. Procaccia, Aaditya Ramdas, Itai Shapira |
| 2024 | Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency. Yiran Liu, Ke Yang, Zehan Qi, Xiao Liu, Yang Yu, Cheng Xiang Zhai |
| 2024 | Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training. Anchit Jain, Rozhin Nobahari, Aristide Baratin, Stefano Sarao Mannelli |
| 2024 | Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding. Jiewen Yang, Yiqun Lin, Bin Pu, Xiaomeng Li |
| 2024 | Bifröst: 3D-Aware Image Compositing with Language Instructions. Lingxiao Li, Kaixiong Gong, Wei-Hong Li, Xili Dai, Tao Chen, Xiaojun Yuan, Xiangyu Yue |
| 2024 | Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control. Michal Nauman, Mateusz Ostaszewski, Krzysztof Jankowski, Piotr Milos, Marek Cygan |
| 2024 | Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature. Tong Zhou, Xuandong Zhao, Xiaolin Xu, Shaolei Ren |
| 2024 | Binarized Diffusion Model for Image Super-Resolution. Zheng Chen, Haotong Qin, Yong Guo, Xiongfei Su, Xin Yuan, Linghe Kong, Yulun Zhang |
| 2024 | Binary Search with Distributional Predictions. Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Aidin Niaparast, Sergei Vassilvitskii |
| 2024 | Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps. Christopher J. Kymn, Sonia Mazelet, Anthony Thomas, Denis Kleyko, Edward Paxon Frady, Fritz Sommer, Bruno A. Olshausen |
| 2024 | Binocular-Guided 3D Gaussian Splatting with View Consistency for Sparse View Synthesis. Liang Han, Junsheng Zhou, Yu-Shen Liu, Zhizhong Han |
| 2024 | BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity. Chih-Hsuan Yang, Benjamin Feuer, Talukder Zaki Jubery, Zi K. Deng, Andre Nakkab, Md. Zahid Hasan, Shivani Chiranjeevi, Kelly O. Marshall, Nirmal Baishnab, Asheesh Kumar Singh, Arti Singh, Soumik Sarkar, Nirav C. Merchant, Chinmay Hegde, Baskar Ganapathysubramanian |
| 2024 | Biologically Inspired Learning Model for Instructed Vision. Roy Abel, Shimon Ullman |
| 2024 | Biomedical Visual Instruction Tuning with Clinician Preference Alignment. Hejie Cui, Lingjun Mao, Xin Liang, Jieyu Zhang, Hui Ren, Quanzheng Li, Xiang Li, Carl Yang |
| 2024 | Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently. Sergio Calo, Anders Jonsson, Gergely Neu, Ludovic Schwartz, Javier Segovia-Aguas |
| 2024 | BitDelta: Your Fine-Tune May Only Be Worth One Bit. James Liu, Guangxuan Xiao, Kai Li, Jason D. Lee, Song Han, Tri Dao, Tianle Cai |
| 2024 | BitsFusion: 1.99 bits Weight Quantization of Diffusion Model. Yang Sui, Yanyu Li, Anil Kag, Yerlan Idelbayev, Junli Cao, Ju Hu, Dhritiman Sagar, Bo Yuan, Sergey Tulyakov, Jian Ren |
| 2024 | Black-Box Forgetting. Yusuke Kuwana, Yuta Goto, Takashi Shibata, Go Irie |
| 2024 | Blind Image Restoration via Fast Diffusion Inversion. Hamadi Chihaoui, Abdelhak Lemkhenter, Paolo Favaro |
| 2024 | Block Sparse Bayesian Learning: A Diversified Scheme. Yanhao Zhang, Zhihan Zhu, Yong Xia |
| 2024 | Block Transformer: Global-to-Local Language Modeling for Fast Inference. Namgyu Ho, Sangmin Bae, Taehyeon Kim, Hyunjik Jo, Yireun Kim, Tal Schuster, Adam Fisch, James Thorne, Se-Young Yun |
| 2024 | BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling. Lin Gui, Cristina Garbacea, Victor Veitch |
| 2024 | BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping. Taolin Zhang, Jinpeng Wang, Hang Guo, Tao Dai, Bin Chen, Shu-Tao Xia |
| 2024 | Boosted Conformal Prediction Intervals. Ran Xie, Rina Barber, Emmanuel J. Candès |
| 2024 | Boosting Alignment for Post-Unlearning Text-to-Image Generative Models. Myeongseob Ko, Henry Li, Zhun Wang, Jonathan Patsenker, Jiachen T. Wang, Qinbin Li, Ming Jin, Dawn Song, Ruoxi Jia |
| 2024 | Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning. Armand Kassaï Koupaï, Jorge Mifsut Benet, Yuan Yin, Jean-Noël Vittaut, Patrick Gallinari |
| 2024 | Boosting Graph Pooling with Persistent Homology. Chaolong Ying, Xinjian Zhao, Tianshu Yu |
| 2024 | Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance. Joshua McClellan, Naveed Haghani, John Winder, Furong Huang, Pratap Tokekar |
| 2024 | Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing. Yadong Qu, Yuxin Wang, Bangbang Zhou, Zixiao Wang, Hongtao Xie, Yongdong Zhang |
| 2024 | Boosting Text-to-Video Generative Model with MLLMs Feedback. Xun Wu, Shaohan Huang, Guolong Wang, Jing Xiong, Furu Wei |
| 2024 | Boosting Transferability and Discriminability for Time Series Domain Adaptation. Mingyang Liu, Xinyang Chen, Yang Shu, Xiucheng Li, Weili Guan, Liqiang Nie |
| 2024 | Boosting Vision-Language Models with Transduction. Maxime Zanella, Benoît Gérin, Ismail Ben Ayed |
| 2024 | Boosting Weakly Supervised Referring Image Segmentation via Progressive Comprehension. Zaiquan Yang, Yuhao Liu, Jiaying Lin, Gerhard P. Hancke, Rynson W. H. Lau |
| 2024 | Boosting the Potential of Large Language Models with an Intelligent Information Assistant. Yujia Zhou, Zheng Liu, Zhicheng Dou |
| 2024 | Boosting the Transferability of Adversarial Attack on Vision Transformer with Adaptive Token Tuning. Di Ming, Peng Ren, Yunlong Wang, Xin Feng |
| 2024 | Bootstrapping Top-down Information for Self-modulating Slot Attention. Dongwon Kim, Seoyeon Kim, Suha Kwak |
| 2024 | Boundary Decomposition for Nadir Objective Vector Estimation. Ruihao Zheng, Zhenkun Wang |
| 2024 | Boundary Matters: A Bi-Level Active Finetuning Method. Han Lu, Yichen Xie, Xiaokang Yang, Junchi Yan |
| 2024 | Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension. Kedar Karhadkar, Michael Murray, Guido F. Montúfar |
| 2024 | Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli. Christopher Wang, Adam Uri Yaari, Aaditya Singh, Vighnesh Subramaniam, Dana Rosenfarb, Jan DeWitt, Pranav Misra, Joseph R. Madsen, Scellig S. Stone, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu |
| 2024 | Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking. Zijian Dong, Ruilin Li, Yilei Wu, Thuan Tinh Nguyen, Joanna Su Xian Chong, Fang Ji, Nathanael Ren Jie Tong, Christopher Li Hsian Chen, Juan Helen Zhou |
| 2024 | BrainBits: How Much of the Brain are Generative Reconstruction Methods Using? David Mayo, Christopher Wang, Asa Harbin, Abdulrahman Alabdulkareem, Albert E. Shaw, Boris Katz, Andrei Barbu |
| 2024 | Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model. Wenjia Xie, Hao Wang, Luankang Zhang, Rui Zhou, Defu Lian, Enhong Chen |
| 2024 | Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts. Zhe Zhao, Haibin Wen, Zikang Wang, Pengkun Wang, Fanfu Wang, Song Lai, Qingfu Zhang, Yang Wang |
| 2024 | Breaking Semantic Artifacts for Generalized AI-generated Image Detection. Chende Zheng, Chenhao Lin, Zhengyu Zhao, Hang Wang, Xu Guo, Shuai Liu, Chao Shen |
| 2024 | Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack. Mingli Zhu, Siyuan Liang, Baoyuan Wu |
| 2024 | Breaking the curse of dimensionality in structured density estimation. Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam |
| 2024 | BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO. Sebastian Dittert, Vincent Moens, Gianni De Fabritiis |
| 2024 | Bridge the Modality and Capability Gaps in Vision-Language Model Selection. Chao Yi, Yuhang He, De-Chuan Zhan, Han-Jia Ye |
| 2024 | Bridge the Points: Graph-based Few-shot Segment Anything Semantically. Anqi Zhang, Guangyu Gao, Jianbo Jiao, Chi Liu, Yunchao Wei |
| 2024 | Bridge-IF: Learning Inverse Protein Folding with Markov Bridges. Yiheng Zhu, Jialu Wu, Qiuyi Li, Jiahuan Yan, Mingze Yin, Wei Wu, Mingyang Li, Jieping Ye, Zheng Wang, Jian Wu |
| 2024 | Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views. Xinyue Chen, Yazhou Ren, Jie Xu, Fangfei Lin, Xiaorong Pu, Yang Yang |
| 2024 | Bridging Geometric States via Geometric Diffusion Bridge. Shengjie Luo, Yixian Xu, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang |
| 2024 | Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models. Masatoshi Uehara, Yulai Zhao, Ehsan Hajiramezanali, Gabriele Scalia, Gökcen Eraslan, Avantika Lal, Sergey Levine, Tommaso Biancalani |
| 2024 | Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. Jiayun Wu, Jiashuo Liu, Peng Cui, Steven Wu |
| 2024 | Bridging OOD Detection and Generalization: A Graph-Theoretic View. Han Wang, Sharon Li |
| 2024 | Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation. Shen Yuan, Haotian Liu, Hongteng Xu |
| 2024 | Bridging semantics and pragmatics in information-theoretic emergent communication. Eleonora Gualdoni, Mycal Tucker, Roger Levy, Noga Zaslavsky |
| 2024 | Bridging the Divide: Reconsidering Softmax and Linear Attention. Dongchen Han, Yifan Pu, Zhuofan Xia, Yizeng Han, Xuran Pan, Xiu Li, Jiwen Lu, Shiji Song, Gao Huang |
| 2024 | BuckTales: A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes. Hemal Naik, Junran Yang, Dipin Das, Margaret Crofoot, Akanksha Rathore, Vivek Hari Sridhar |
| 2024 | Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models. Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E. Gonzalez, Bin Cui |
| 2024 | Building Timeseries Dataset: Empowering Large-Scale Building Analytics. Arian Prabowo, Xiachong Lin, Imran Razzak, Hao Xue, Emily W. Yap, Matthew Amos, Flora D. Salim |
| 2024 | Building a stable classifier with the inflated argmax. Jake A. Soloff, Rina Barber, Rebecca Willett |
| 2024 | Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers. Xiuying Wei, Skander Moalla, Razvan Pascanu, Caglar Gulcehre |
| 2024 | Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences. Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov |
| 2024 | C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory. Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen, Jun Zhu |
| 2024 | CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing. Yen-Ju Lu, Jing Liu, Thomas Thebaud, Laureano Moro-Velázquez, Ariya Rastrow, Najim Dehak, Jesús Villalba |
| 2024 | CALANet: Cheap All-Layer Aggregation for Human Activity Recognition. Jaegyun Park, Dae-Won Kim, Jaesung Lee |
| 2024 | CALE: Continuous Arcade Learning Environment. Jesse Farebrother, Pablo Samuel Castro |
| 2024 | CALVIN: Improved Contextual Video Captioning via Instruction Tuning. Gowthami Somepalli, Arkabandhu Chowdhury, Jonas Geiping, Ronen Basri, Tom Goldstein, David Jacobs |
| 2024 | CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes. Jason Yang, Ariane Mora, Shengchao Liu, Bruce J. Wittmann, Animashree Anandkumar, Frances H. Arnold, Yisong Yue |
| 2024 | CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models. Peng Xia, Ze Chen, Juanxi Tian, Yangrui Gong, Ruibo Hou, Yue Xu, Zhenbang Wu, Zhiyuan Fan, Yiyang Zhou, Kangyu Zhu, Wenhao Zheng, Zhaoyang Wang, Xiao Wang, Xuchao Zhang, Chetan Bansal, Marc Niethammer, Junzhou Huang, Hongtu Zhu, Yun Li, Jimeng Sun, Zongyuan Ge, Gang Li, James Y. Zou, Huaxiu Yao |
| 2024 | CAT3D: Create Anything in 3D with Multi-View Diffusion Models. Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, Ben Poole |
| 2024 | CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation. Zhongzhen Huang, Yankai Jiang, Rongzhao Zhang, Shaoting Zhang, Xiaofan Zhang |
| 2024 | CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework. Yiyang Zhao, Yunzhuo Liu, Bo Jiang, Tian Guo |
| 2024 | CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition. Yuhang Wen, Mengyuan Liu, Songtao Wu, Beichen Ding |
| 2024 | CIFD: Controlled Information Flow to Enhance Knowledge Distillation. Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Jaejin Cho, Ching Hua Lee, Chouchang Yang, Yilin Shen, Hongxia Jin |
| 2024 | CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models. Saurav Jha, Dong Gong, Lina Yao |
| 2024 | CLAVE: An Adaptive Framework for Evaluating Values of LLM Generated Responses. Jing Yao, Xiaoyuan Yi, Xing Xie |
| 2024 | CLIP in Mirror: Disentangling text from visual images through reflection. Tiancheng Wang, Yuguang Yang, Linlin Yang, Shaohui Lin, Juan Zhang, Guodong Guo, Baochang Zhang |
| 2024 | CLIPAway: Harmonizing focused embeddings for removing objects via diffusion models. Yigit Ekin, Ahmet Burak Yildirim, Erdem Eren Caglar, Aykut Erdem, Erkut Erdem, Aysegul Dundar |
| 2024 | CLIPCEIL: Domain Generalization through CLIP via Channel rEfinement and Image-text aLignment. Xi Yu, Shinjae Yoo, Yuewei Lin |
| 2024 | CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning. Yiping Wang, Yifang Chen, Wendan Yan, Alex Fang, Wenjing Zhou, Kevin Jamieson, Simon S. Du |
| 2024 | CLUES: Collaborative Private-domain High-quality Data Selection for LLMs via Training Dynamics. Wanru Zhao, Hongxiang Fan, Shell Xu Hu, Wangchunshu Zhou, Nicholas D. Lane |
| 2024 | CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors. Linye Lyu, Jiawei Zhou, Daojing He, Yu Li |
| 2024 | CODA: A Correlation-Oriented Disentanglement and Augmentation Modeling Scheme for Better Resisting Subpopulation Shifts. Ziquan Ou, Zijun Zhang |
| 2024 | CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models. Junho Kim, Hyunjun Kim, Yeonju Kim, Yong Man Ro |
| 2024 | COLD: Causal reasOning in cLosed Daily activities. Abhinav Joshi, Areeb Ahmad, Ashutosh Modi |
| 2024 | CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions. Sk Miraj Ahmed, Fahim Faisal Niloy, Xiangyu Chang, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury |
| 2024 | COSMIC: Compress Satellite Image Efficiently via Diffusion Compensation. Ziyuan Zhang, Han Qiu, Maosen Zhang, Jun Liu, Bin Chen, Tianwei Zhang, Hewu Li |
| 2024 | COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing. Jiangshan Wang, Yue Ma, Jiayi Guo, Yicheng Xiao, Gao Huang, Xiu Li |
| 2024 | CRAG - Comprehensive RAG Benchmark. Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Daniel Gui, Ziran Will Jiang, Ziyu Jiang, Lingkun Kong, Brian Moran, Jiaqi Wang, Yifan Xu, An Yan, Chenyu Yang, Eting Yuan, Hanwen Zha, Nan Tang, Lei Chen, Nicolas Scheffer, Yue Liu, Nirav Shah, Rakesh Wanga, Anuj Kumar, Scott Yih, Xin Dong |
| 2024 | CRAYM: Neural Field Optimization via Camera RAY Matching. Liqiang Lin, Wenpeng Wu, Chi-Wing Fu, Hao Zhang, Hui Huang |
| 2024 | CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks. Miria Feng, Zachary Frangella, Mert Pilanci |
| 2024 | CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection. Jisong Kim, Minjae Seong, Jun Won Choi |
| 2024 | CSPG: Crossing Sparse Proximity Graphs for Approximate Nearest Neighbor Search. Ming Yang, Yuzheng Cai, Weiguo Zheng |
| 2024 | CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence. Md Tanvirul Alam, Dipkamal Bhusal, Le Nguyen, Nidhi Rastogi |
| 2024 | CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence. Chaochao Chen, Jiaming Zhang, Yizhao Zhang, Li Zhang, Lingjuan Lyu, Yuyuan Li, Biao Gong, Chenggang Yan |
| 2024 | CV-VAE: A Compatible Video VAE for Latent Generative Video Models. Sijie Zhao, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Muyao Niu, Xiaoyu Li, Wenbo Hu, Ying Shan |
| 2024 | CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark. David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo, Santiago Góngora, Aishik Mandal, Sukannya Purkayastha, Jesús-Germán Ortiz-Barajas, Emilio Villa-Cueva, Jinheon Baek, Soyeong Jeong, Injy Hamed, Zheng Xin Yong, Zheng Wei Lim, Paula Mónica Silva, Jocelyn Dunstan, Mélanie Jouitteau, David Le Meur, Joan Nwatu, Ganzorig Batnasan, Munkh-Erdene Otgonbold, Munkhjargal Gochoo, Guido Ivetta, Luciana Benotti, Laura Alonso Alemany, Hernán Maina, Jiahui Geng, Tiago Timponi Torrent, Frederico Belcavello, Marcelo Viridiano, Jan Christian Blaise Cruz, Dan John Velasco, Oana Ignat, Zara Burzo, Chenxi Whitehouse, Artem Abzaliev, Teresa Clifford, Grainne Caulfield, Teresa Lynn, Christian Salamea Palacios, Vladimir Araujo, Yova Kementchedjhieva, Mihail Mihaylov, Israel Abebe Azime, Henok Biadglign Ademtew, Bontu Fufa Balcha, Naome A. Etori, David Ifeoluwa Adelani, Rada Mihalcea, Atnafu Lambebo Tonja, Maria Camila Buitrago Cabrera, Gisela Vallejo, Holy Lovenia, Ruochen Zhang, Marcos Estecha-Garitagoitia, Mario Rodríguez-Cantelar, Toqeer Ehsan, Rendi Chevi, Muhammad Farid Adilazuarda, Ryandito Diandaru, Samuel Cahyawijaya, Fajri Koto, Tatsuki Kuribayashi, Haiyue Song, Aditya Khandavally, Thanmay Jayakumar, Raj Dabre, Mohamed Fazli Mohamed Imam, Kumaranage Ravindu Yasas Nagasinghe, Alina Dragonetti, Luis Fernando D'Haro, Olivier Niyomugisha, Jay Gala, Pranjal A. Chitale, Fauzan Farooqui, Thamar Solorio, Alham Fikri Aji |
| 2024 | CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos. Trong-Thuan Nguyen, Pha A. Nguyen, Xin Li, Jackson David Cothren, Alper Yilmaz, Khoa Luu |
| 2024 | CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset. Akshatha Arodi, Margaux Luck, Jean-Luc Bedwani, Aldo Zaimi, Ge Li, Nicolas Pouliot, Julien Beaudry, Gaétan Marceau-Caron |
| 2024 | Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment. Teng Xiao, Yige Yuan, Huaisheng Zhu, Mingxiao Li, Vasant G. Honavar |
| 2024 | Calibrated Self-Rewarding Vision Language Models. Yiyang Zhou, Zhiyuan Fan, Dongjie Cheng, Sihan Yang, Zhaorun Chen, Chenhang Cui, Xiyao Wang, Yun Li, Linjun Zhang, Huaxiu Yao |
| 2024 | Calibrating Reasoning in Language Models with Internal Consistency. Zhihui Xie, Jizhou Guo, Tong Yu, Shuai Li |
| 2024 | Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs. Peter Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Adithya Iyer, Sai Charitha Akula, Shusheng Yang, Jihan Yang, Manoj Middepogu, Ziteng Wang, Xichen Pan, Rob Fergus, Yann LeCun, Saining Xie |
| 2024 | Can Graph Learning Improve Planning in LLM-based Agents? Xixi Wu, Yifei Shen, Caihua Shan, Kaitao Song, Siwei Wang, Bohang Zhang, Jiarui Feng, Hong Cheng, Wei Chen, Yun Xiong, Dongsheng Li |
| 2024 | Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach. Hanyang Yuan, Jiarong Xu, Renhong Huang, Mingli Song, Chunping Wang, Yang Yang |
| 2024 | Can LLMs Implicitly Learn Numeric Parameter Constraints in Data Science APIs? Yinlin Deng, Chunqiu Steven Xia, Zhezhen Cao, Meiziniu Li, Lingming Zhang |
| 2024 | Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study. Xuefei Ning, Zifu Wang, Shiyao Li, Zinan Lin, Peiran Yao, Tianyu Fu, Matthew B. Blaschko, Guohao Dai, Huazhong Yang, Yu Wang |
| 2024 | Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation. Kehan Guo, Bozhao Nan, Yujun Zhou, Taicheng Guo, Zhichun Guo, Mihir Surve, Zhenwen Liang, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang |
| 2024 | Can Language Models Learn to Skip Steps? Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Cheng Jiayang, Yue Zhang, Xipeng Qiu, Zheng Zhang |
| 2024 | Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales? Zhanke Zhou, Rong Tao, Jianing Zhu, Yiwen Luo, Zengmao Wang, Bo Han |
| 2024 | Can Large Language Model Agents Simulate Human Trust Behavior? Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Shiyang Lai, Kai Shu, Jindong Gu, Adel Bibi, Ziniu Hu, David Jurgens, James Evans, Philip Torr, Bernard Ghanem, Guohao Li |
| 2024 | Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models. Xin Li, Weize Chen, Qizhi Chu, Haopeng Li, Zhaojun Sun, Ran Li, Chen Qian, Yiwei Wei, Chuan Shi, Zhiyuan Liu, Maosong Sun, Cheng Yang |
| 2024 | Can Learned Optimization Make Reinforcement Learning Less Difficult? Alexander David Goldie, Chris Lu, Matthew Thomas Jackson, Shimon Whiteson, Jakob N. Foerster |
| 2024 | Can Models Learn Skill Composition from Examples? Haoyu Zhao, Simran Kaur, Dingli Yu, Anirudh Goyal, Sanjeev Arora |
| 2024 | Can Simple Averaging Defeat Modern Watermarks? Pei Yang, Hai Ci, Yiren Song, Mike Zheng Shou |
| 2024 | Can Transformers Smell Like Humans? Farzaneh Taleb, Miguel Vasco, Antônio H. Ribeiro, Mårten Björkman, Danica Kragic |
| 2024 | Can We Leave Deepfake Data Behind in Training Deepfake Detector? Jikang Cheng, Zhiyuan Yan, Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li |
| 2024 | Can an AI Agent Safely Run a Government? Existence of Probably Approximately Aligned Policies. Frédéric Berdoz, Roger Wattenhofer |
| 2024 | Can large language models explore in-context? Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins |
| 2024 | Can neural operators always be continuously discretized? Takashi Furuya, Michael Puthawala, Matti Lassas, Maarten V. de Hoop |
| 2024 | CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities. Rohith Peddi, Shivvrat Arya, Bharath Challa, Likhitha Pallapothula, Akshay Vyas, Bhavya Gouripeddi, Qifan Zhang, Jikai Wang, Vasundhara Komaragiri, Eric D. Ragan, Nicholas Ruozzi, Yu Xiang, Vibhav Gogate |
| 2024 | Capturing the denoising effect of PCA via compression ratio. Chandra Sekhar Mukherjee, Nikhil Deorkar, Jiapeng Zhang |
| 2024 | Cardinality-Aware Set Prediction and Top-$k$ Classification. Corinna Cortes, Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong |
| 2024 | Carrot and Stick: Eliciting Comparison Data and Beyond. Yiling Chen, Shi Feng, Fang-Yi Yu |
| 2024 | Cascade Speculative Drafting for Even Faster LLM Inference. Ziyi Chen, Xiaocong Yang, Jiacheng Lin, Chenkai Sun, Kevin Chen-Chuan Chang, Jie Huang |
| 2024 | Cascade of phase transitions in the training of energy-based models. Dimitrios Bachtis, Giulio Biroli, Aurélien Decelle, Beatriz Seoane |
| 2024 | Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification. Thomas Kwa, Drake Thomas, Adrià Garriga-Alonso |
| 2024 | Categorical Flow Matching on Statistical Manifolds. Chaoran Cheng, Jiahan Li, Jian Peng, Ge Liu |
| 2024 | Causal Context Adjustment Loss for Learned Image Compression. Minghao Han, Shiyin Jiang, Shengxi Li, Xin Deng, Mai Xu, Ce Zhu, Shuhang Gu |
| 2024 | Causal Contrastive Learning for Counterfactual Regression Over Time. Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit, Paul-Henry Cournède |
| 2024 | Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model. Yifan Duan, Jian Zhao, pengcheng, Junyuan Mao, Hao Wu, Jingyu Xu, Shilong Wang, Caoyuan Ma, Kai Wang, Kun Wang, Xuelong Li |
| 2024 | Causal Dependence Plots. Joshua R. Loftus, Lucius Bynum, Sakina Hansen |
| 2024 | Causal Discovery from Event Sequences by Local Cause-Effect Attribution. Joscha Cüppers, Sascha Xu, Ahmed Musa, Jilles Vreeken |
| 2024 | Causal Effect Identification in a Sub-Population with Latent Variables. Amir Mohammad Abouei, Ehsan Mokhtarian, Negar Kiyavash, Matthias Grossglauser |
| 2024 | Causal Imitation for Markov Decision Processes: a Partial Identification Approach. Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim |
| 2024 | Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects. Alexander Levis, Gabriel Loewinger, Francisco Pereira |
| 2024 | Causal Temporal Representation Learning with Nonstationary Sparse Transition. Xiangchen Song, Zijian Li, Guangyi Chen, Yujia Zheng, Yewen Fan, Xinshuai Dong, Kun Zhang |
| 2024 | Causal discovery with endogenous context variables. Wiebke Günther, Oana-Iuliana Popescu, Martin Rabel, Urmi Ninad, Andreas Gerhardus, Jakob Runge |
| 2024 | Causal language modeling can elicit search and reasoning capabilities on logic puzzles. Kulin Shah, Nishanth Dikkala, Xin Wang, Rina Panigrahy |
| 2024 | Causal vs. Anticausal merging of predictors. Sergio Hernan Garrido Mejia, Patrick Blöbaum, Bernhard Schölkopf, Dominik Janzing |
| 2024 | CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes. Paritosh Parmar, Eric Peh, Ruirui Chen, Ting En Lam, Yuhan Chen, Elston Tan, Basura Fernando |
| 2024 | CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense. Mingkun Zhang, Keping Bi, Wei Chen, Quanrun Chen, Jiafeng Guo, Xueqi Cheng |
| 2024 | CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction. Shuqi Li, Yuebo Sun, Yuxin Lin, Xin Gao, Shuo Shang, Rui Yan |
| 2024 | Cell ontology guided transcriptome foundation model. Xinyu Yuan, Zhihao Zhan, Zuobai Zhang, Manqi Zhou, Jianan Zhao, Boyu Han, Yue Li, Jian Tang |
| 2024 | CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition. Zhonglin Sun, Siyang Song, Ioannis Patras, Georgios Tzimiropoulos |
| 2024 | Certified Adversarial Robustness via Randomized α-Smoothing for Regression Models. Aref Miri Rekavandi, Farhad Farokhi, Olga Ohrimenko, Benjamin I. P. Rubinstein |
| 2024 | Certified Machine Unlearning via Noisy Stochastic Gradient Descent. Eli Chien, Haoyu Wang, Ziang Chen, Pan Li |
| 2024 | Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing. Weizhi Gao, Zhichao Hou, Han Xu, Xiaorui Liu |
| 2024 | Chain of Agents: Large Language Models Collaborating on Long-Context Tasks. Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang, Sercan Ö. Arik |
| 2024 | Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs. Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin |
| 2024 | Chain of Thoughtlessness? An Analysis of CoT in Planning. Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati |
| 2024 | Chain-of-Thought Reasoning Without Prompting. Xuezhi Wang, Denny Zhou |
| 2024 | Challenges of Generating Structurally Diverse Graphs. Fedor Velikonivtsev, Mikhail Mironov, Liudmila Prokhorenkova |
| 2024 | Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization. Dang Nguyen, Paymon Haddad, Eric Gan, Baharan Mirzasoleiman |
| 2024 | ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction. Juan Nathaniel, Yongquan Qu, Tung Nguyen, Sungduk Yu, Julius Busecke, Aditya Grover, Pierre Gentine |
| 2024 | CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs. Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, Alexis Chevalier, Sanjeev Arora, Danqi Chen |
| 2024 | Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers. Haifeng Huang, Yilun Chen, Zehan Wang, Rongjie Huang, Runsen Xu, Tai Wang, Luping Liu, Xize Cheng, Yang Zhao, Jiangmiao Pang, Zhou Zhao |
| 2024 | ChatCam: Empowering Camera Control through Conversational AI. Xinhang Liu, Yu-Wing Tai, Chi-Keung Tang |
| 2024 | ChatQA: Surpassing GPT-4 on Conversational QA and RAG. Zihan Liu, Wei Ping, Rajarshi Roy, Peng Xu, Chankyu Lee, Mohammad Shoeybi, Bryan Catanzaro |
| 2024 | ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model. Yiming Sun, Fan Yu, Shaoxiang Chen, Yu Zhang, Junwei Huang, Yang Li, Chenhui Li, Changbo Wang |
| 2024 | Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models. Wanyun Cui, Qianle Wang |
| 2024 | Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models. Ali Behrouz, Michele Santacatterina, Ramin Zabih |
| 2024 | ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions. Etienne Vareille, Michele Linardi, Ioannis Tsamardinos, Vassilis Christophides |
| 2024 | ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation. Shenghai Yuan, Jinfa Huang, Yongqi Xu, Yaoyang Liu, Shaofeng Zhang, Yujun Shi, Ruijie Zhu, Xinhua Cheng, Jiebo Luo, Li Yuan |
| 2024 | CigTime: Corrective Instruction Generation Through Inverse Motion Editing. Qihang Fang, Chengcheng Tang, Bugra Tekin, Yanchao Yang |
| 2024 | CiteME: Can Language Models Accurately Cite Scientific Claims? Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, Matthias Bethge |
| 2024 | ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence. Kevin Wu, Eric Wu, James Y. Zou |
| 2024 | Class Distribution Shifts in Zero-Shot Learning: Learning Robust Representations. Yuli Slavutsky, Yuval Benjamini |
| 2024 | Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification. Yuankai Luo, Lei Shi, Xiao-Ming Wu |
| 2024 | Classification Diffusion Models: Revitalizing Density Ratio Estimation. Shahar Yadin, Noam Elata, Tomer Michaeli |
| 2024 | Classification Done Right for Vision-Language Pre-Training. Zilong Huang, Qinghao Ye, Bingyi Kang, Jiashi Feng, Haoqi Fan |
| 2024 | Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift. Junbao Chen, Jingfeng Xue, Yong Wang, Zhenyan Liu, Lu Huang |
| 2024 | Classifier-guided Gradient Modulation for Enhanced Multimodal Learning. Zirun Guo, Tao Jin, Jingyuan Chen, Zhou Zhao |
| 2024 | ClavaDDPM: Multi-relational Data Synthesis with Cluster-guided Diffusion Models. Wei Pang, Masoumeh Shafieinejad, Lucy Liu, Stephanie Hazlewood, Xi He |
| 2024 | CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making. Zibin Dong, Yifu Yuan, Jianye Hao, Fei Ni, Yi Ma, Pengyi Li, Yan Zheng |
| 2024 | ClevrSkills: Compositional Language And Visual Reasoning in Robotics. Sanjay Haresh, Daniel Dijkman, Apratim Bhattacharyya, Roland Memisevic |
| 2024 | Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation. Qingwen Bu, Jia Zeng, Li Chen, Yanchao Yang, Guyue Zhou, Junchi Yan, Ping Luo, Heming Cui, Yi Ma, Hongyang Li |
| 2024 | Cloud Object Detector Adaptation by Integrating Different Source Knowledge. Shuaifeng Li, Mao Ye, Lihua Zhou, Nianxin Li, Siying Xiao, Song Tang, Xiatian Zhu |
| 2024 | Cluster-Learngene: Inheriting Adaptive Clusters for Vision Transformers. Qiufeng Wang, Xu Yang, Fu Feng, Jing Wang, Xin Geng |
| 2024 | Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention. Siyuan Huang, Yunchong Song, Jiayue Zhou, Zhouhan Lin |
| 2024 | Clustering in Causal Attention Masking. Nikita Karagodin, Yury Polyanskiy, Philippe Rigollet |
| 2024 | Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding. Ke Liang, Yue Liu, Hao Li, Lingyuan Meng, Suyuan Liu, Siwei Wang, Sihang Zhou, Xinwang Liu |
| 2024 | Clustering with Non-adaptive Subset Queries. Hadley Black, Euiwoong Lee, Arya Mazumdar, Barna Saha |
| 2024 | Co-occurrence is not Factual Association in Language Models. Xiao Zhang, Miao Li, Ji Wu |
| 2024 | CoBo: Collaborative Learning via Bilevel Optimization. Diba Hashemi, Lie He, Martin Jaggi |
| 2024 | CoFie: Learning Compact Neural Surface Representations with Coordinate Fields. Hanwen Jiang, Haitao Yang, Georgios Pavlakos, Qixing Huang |
| 2024 | CoIN: A Benchmark of Continual Instruction Tuning for Multimodel Large Language Models. Cheng Chen, Junchen Zhu, Xu Luo, Hengtao Shen, Jingkuan Song, Lianli Gao |
| 2024 | CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade |
| 2024 | CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization. Zi Yang, Ziyue Liu, Samridhi Choudhary, Xinfeng Xie, Cao Gao, Siegfried Kunzmann, Zheng Zhang |
| 2024 | CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching. Dongzhi Jiang, Guanglu Song, Xiaoshi Wu, Renrui Zhang, Dazhong Shen, Zhuofan Zong, Yu Liu, Hongsheng Li |
| 2024 | CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding. Emanuele Vivoli, Marco Bertini, Dimosthenis Karatzas |
| 2024 | CoSW: Conditional Sample Weighting for Smoke Segmentation with Label Noise. Lujian Yao, Haitao Zhao, Zhongze Wang, Kaijie Zhao, Jingchao Peng |
| 2024 | CoSy: Evaluating Textual Explanations of Neurons. Laura Kopf, Philine Lou Bommer, Anna Hedström, Sebastian Lapuschkin, Marina M.-C. Höhne, Kirill Bykov |
| 2024 | CoVoMix: Advancing Zero-Shot Speech Generation for Human-like Multi-talker Conversations. Leying Zhang, Yao Qian, Long Zhou, Shujie Liu, Dongmei Wang, Xiaofei Wang, Midia Yousefi, Yanmin Qian, Jinyu Li, Lei He, Sheng Zhao, Michael Zeng |
| 2024 | Coarse-to-Fine Concept Bottleneck Models. Konstantinos P. Panousis, Dino Ienco, Diego Marcos |
| 2024 | Code Repair with LLMs gives an Exploration-Exploitation Tradeoff. Hao Tang, Keya Hu, Jin Zhou, Sicheng Zhong, Wei-Long Zheng, Xujie Si, Kevin Ellis |
| 2024 | CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming. Ali TehraniJamsaz, Arijit Bhattacharjee, Le Chen, Nesreen K. Ahmed, Amir Yazdanbakhsh, Ali Jannesari |
| 2024 | Codec Avatar Studio: Paired Human Captures for Complete, Driveable, and Generalizable Avatars. Julieta Martinez, Emily Kim, Javier Romero, Timur M. Bagautdinov, Shunsuke Saito, Shoou-I Yu, Stuart Anderson, Michael Zollhöfer, Te-Li Wang, Shaojie Bai, Chenghui Li, Shih-En Wei, Rohan Joshi, Wyatt Borsos, Tomas Simon, Jason M. Saragih, Paul Theodosis, Alexander Greene, Anjani Josyula, Silvio Maeta, Andrew Jewett, Simion Venshtain, Christopher Heilman, Yueh-Tung Chen, Sidi Fu, Mohamed Elshaer, Tingfang Du, Longhua Wu, Shen-Chi Chen, Kai Kang, Michael Wu, Youssef Emad, Steven Longay, Ashley Brewer, Hitesh Shah, James Booth, Taylor Koska, Kayla Haidle, Matthew Andromalos, Joanna Hsu, Thomas Dauer, Peter Selednik, Timothy Godisart, Scott Ardisson, Matthew Cipperly, Ben Humberston, Lon Farr, Bob Hansen, Peihong Guo, Dave Braun, Steven Krenn, He Wen, Lucas Evans, Natalia Fadeeva, Matthew Stewart, Gabriel Schwartz, Divam Gupta, Gyeongsik Moon, Kaiwen Guo, Yuan Dong, Yichen Xu, Takaaki Shiratori, Fabian Prada, Bernardo Pires, Bo Peng, Julia Buffalini, Autumn Trimble, Kevyn McPhail, Melissa Schoeller, Yaser Sheikh |
| 2024 | Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework. Parsa Moradi, Behrooz Tahmasebi, Mohammad Ali Maddah-Ali |
| 2024 | Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning. Hao Ma, Tianyi Hu, Zhiqiang Pu, Boyin Liu, Xiaolin Ai, Yanyan Liang, Min Chen |
| 2024 | CogVLM: Visual Expert for Pretrained Language Models. Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, Jiazheng Xu, Keqin Chen, Bin Xu, Juanzi Li, Yuxiao Dong, Ming Ding, Jie Tang |
| 2024 | Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models. Hao Yan, Keith Levin |
| 2024 | Coherent 3D Scene Diffusion From a Single RGB Image. Manuel Dahnert, Angela Dai, Norman Müller, Matthias Nießner |
| 2024 | ColJailBreak: Collaborative Generation and Editing for Jailbreaking Text-to-Image Deep Generation. Yizhuo Ma, Shanmin Pang, Qi Guo, Tianyu Wei, Qing Guo |
| 2024 | Collaboration! Towards Robust Neural Methods for Routing Problems. Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen |
| 2024 | Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling. Weibo Gao, Qi Liu, Linan Yue, Fangzhou Yao, Hao Wang, Yin Gu, Zheng Zhang |
| 2024 | Collaborative Refining for Learning from Inaccurate Labels. Bin Han, Yi-Xuan Sun, Ya-Lin Zhang, Libang Zhang, Haoran Hu, Longfei Li, Jun Zhou, Guo Ye, Huimei He |
| 2024 | Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control. Zhengfei Kuang, Shengqu Cai, Hao He, Yinghao Xu, Hongsheng Li, Leonidas J. Guibas, Gordon Wetzstein |
| 2024 | Color-Oriented Redundancy Reduction in Dataset Distillation. Bowen Yuan, Zijian Wang, Mahsa Baktashmotlagh, Yadan Luo, Zi Huang |
| 2024 | ComBack: A Versatile Dataset for Enhancing Compiler Backend Development Efficiency. Ming Zhong, Fang Lyu, Lulin Wang, Hongna Geng, Lei Qiu, Huimin Cui, Xiaobing Feng |
| 2024 | Combining Observational Data and Language for Species Range Estimation. Max Hamilton, Christian Lange, Elijah Cole, Alexander Shepard, Samuel Heinrich, Oisin Mac Aodha, Grant Van Horn, Subhransu Maji |
| 2024 | Combining Statistical Depth and Fermat Distance for Uncertainty Quantification. Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi, Sixin Zhang, Serge Gratton, Thierry Giaccone |
| 2024 | Communication Bounds for the Distributed Experts Problem. Zhihao Jia, Qi Pang, Trung Tran, David P. Woodruff, Zhihao Zhang, Wenting Zheng |
| 2024 | Communication Efficient Distributed Training with Distributed Lion. Bo Liu, Lemeng Wu, Lizhang Chen, Kaizhao Liang, Jiaxu Zhu, Chen Liang, Raghuraman Krishnamoorthi, Qiang Liu |
| 2024 | Communication-Efficient Federated Group Distributionally Robust Optimization. Zhishuai Guo, Tianbao Yang |
| 2024 | Community Detection Guarantees using Embeddings Learned by Node2Vec. Andrew Davison, S. Carlyle Morgan, Owen G. Ward |
| 2024 | Compact Language Models via Pruning and Knowledge Distillation. Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov |
| 2024 | Compact Proofs of Model Performance via Mechanistic Interpretability. Jason Gross, Rajashree Agrawal, Thomas Kwa, Euan Ong, Chun Hei Yip, Alex Gibson, Soufiane Noubir, Lawrence Chan |
| 2024 | Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference. Yonghan Jung, Min Woo Park, Sanghack Lee |
| 2024 | Compositional 3D-aware Video Generation with LLM Director. Hanxin Zhu, Tianyu He, Anni Tang, Junliang Guo, Zhibo Chen, Jiang Bian |
| 2024 | Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning. Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte, Sanjit A. Seshia |
| 2024 | Compositional Generalization Across Distributional Shifts with Sparse Tree Operations. Paul Soulos, Henry Conklin, Mattia Opper, Paul Smolensky, Jianfeng Gao, Roland Fernandez |
| 2024 | Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond. Kirill Brilliantov, Amauri H. Souza, Vikas Garg |
| 2024 | Compressing Large Language Models using Low Rank and Low Precision Decomposition. Rajarshi Saha, Naomi Sagan, Varun Srivastava, Andrea Goldsmith, Mert Pilanci |
| 2024 | Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham |
| 2024 | Computational Aspects of Bayesian Persuasion under Approximate Best Response. Kunhe Yang, Hanrui Zhang |
| 2024 | Computerized Adaptive Testing via Collaborative Ranking. Zirui Liu, Yan Zhuang, Qi Liu, Jiatong Li, Yuren Zhang, Zhenya Huang, Jinze Wu, Shijin Wang |
| 2024 | Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise. Sebastian Allmeier, Nicolas Gast |
| 2024 | Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification. Junru Chen, Tianyu Cao, Jing Xu, Jiahe Li, Zhilong Chen, Tao Xiao, Yang Yang |
| 2024 | ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs. Irene Huang, Wei Lin, Muhammad Jehanzeb Mirza, Jacob A. Hansen, Sivan Doveh, Victor Butoi, Roei Herzig, Assaf Arbelle, Hilde Kuehne, Trevor Darrell, Chuang Gan, Aude Oliva, Rogério Feris, Leonid Karlinsky |
| 2024 | ConStat: Performance-Based Contamination Detection in Large Language Models. Jasper Dekoninck, Mark Niklas Müller, Martin T. Vechev |
| 2024 | Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Models. Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Chen Liu, Yu Lan, Chao Shen |
| 2024 | ConceptFactory: Facilitate 3D Object Knowledge Annotation with Object Conceptualization. Jianhua Sun, Yuxuan Li, Longfei Xu, Nange Wang, Jiude Wei, Yining Zhang, Cewu Lu |
| 2024 | ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty. Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, Sanjeev Arora |
| 2024 | CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting. Jianrong Ding, Zhanyu Liu, Guanjie Zheng, Haiming Jin, Linghe Kong |
| 2024 | Conditional Controllable Image Fusion. Bing Cao, Xingxin Xu, Pengfei Zhu, Qilong Wang, Qinghua Hu |
| 2024 | Conditional Density Estimation with Histogram Trees. Lincen Yang, Matthijs van Leeuwen |
| 2024 | Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand. Md. Musfiqur Rahman, Matt Jordan, Murat Kocaoglu |
| 2024 | Conditional Outcome Equivalence: A Quantile Alternative to CATE. Josh Givens, Henry W. J. Reeve, Song Liu, Katarzyna Reluga |
| 2024 | Conditional Synthesis of 3D Molecules with Time Correction Sampler. Hojung Jung, Youngrok Park, Laura Schmid, Jaehyeong Jo, Dongkyu Lee, Bongsang Kim, Se-Young Yun, Jinwoo Shin |
| 2024 | Conditioning non-linear and infinite-dimensional diffusion processes. Elizabeth Louise Baker, Gefan Yang, Michael L. Severinsen, Christy Anna Hipsley, Stefan Sommer |
| 2024 | Confidence Calibration of Classifiers with Many Classes. Adrien Le-Coz, Stéphane Herbin, Faouzi Adjed |
| 2024 | Confidence Regulation Neurons in Language Models. Alessandro Stolfo, Ben Wu, Wes Gurnee, Yonatan Belinkov, Xingyi Song, Mrinmaya Sachan, Neel Nanda |
| 2024 | Confident Natural Policy Gradient for Local Planning in q Tian Tian, Lin Yang, Csaba Szepesvári |
| 2024 | ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLMs. Zhaochen Su, Jun Zhang, Xiaoye Qu, Tong Zhu, Yanshu Li, Jiashuo Sun, Juntao Li, Min Zhang, Yu Cheng |
| 2024 | Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees. Yu Gui, Ying Jin, Zhimei Ren |
| 2024 | Conformal Classification with Equalized Coverage for Adaptively Selected Groups. Yanfei Zhou, Matteo Sesia |
| 2024 | Conformal Inverse Optimization. Bo Lin, Erick Delage, Timothy C. Y. Chan |
| 2024 | Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration. Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja, Jana Doppa, Yan Yan |
| 2024 | Conformalized Credal Set Predictors. Alireza Javanmardi, David Stutz, Eyke Hüllermeier |
| 2024 | Conformalized Multiple Testing after Data-dependent Selection. Xiaoning Wang, Yuyang Huo, Liuhua Peng, Changliang Zou |
| 2024 | Conformalized Time Series with Semantic Features. Baiting Chen, Zhimei Ren, Lu Cheng |
| 2024 | Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data. Xiaohong Chen, Canran Xiao, Yongmei Liu |
| 2024 | Conjugate Bayesian Two-step Change Point Detection for Hawkes Process. Zeyue Zhang, Xiaoling Lu, Feng Zhou |
| 2024 | Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models. Mengyuan Chen, Junyu Gao, Changsheng Xu |
| 2024 | Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning. Shentong Mo, Peter Tong |
| 2024 | Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. Johannes Treutlein, Dami Choi, Jan Betley, Samuel Marks, Cem Anil, Roger B. Grosse, Owain Evans |
| 2024 | Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion. Zhiwei Bai, Jiajie Zhao, Yaoyu Zhang |
| 2024 | Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Domain Segmenters. Dong Zhao, Qi Zang, Shuang Wang, Nicu Sebe, Zhun Zhong |
| 2024 | Consensus Learning with Deep Sets for Essential Matrix Estimation. Dror Moran, Yuval Margalit, Guy Trostianetsky, Fadi Khatib, Meirav Galun, Ronen Basri |
| 2024 | Consent in Crisis: The Rapid Decline of the AI Data Commons. Shayne Longpre, Robert Mahari, Ariel Lee, Campbell Lund, Hamidah Oderinwale, William Brannon, Nayan Saxena, Naana Obeng-Marnu, Tobin South, Cole Hunter, Kevin Klyman, Christopher Klamm, Hailey Schoelkopf, Nikhil Singh, Manuel Cherep, Ahmad Anis, An Dinh, Caroline Shamiso Chitongo, Da Yin, Damien Sileo, Deividas Mataciunas, Diganta Misra, Emad A. Alghamdi, Enrico Shippole, Jianguo Zhang, Joanna Materzynska, Kun Qian, Kushagra Tiwary, Lester James V. Miranda, Manan Dey, Minnie Liang, Mohammed Hamdy, Niklas Muennighoff, Seonghyeon Ye, Seungone Kim, Shrestha Mohanty, Vipul Gupta, Vivek Sharma, Minh Chien Vu, Xuhui Zhou, Yizhi Li, Caiming Xiong, Luis Villa, Stella Biderman, Hanlin Li, Daphne Ippolito, Sara Hooker, Jad Kabbara, Alex Pentland |
| 2024 | Consistency Diffusion Bridge Models. Guande He, Kaiwen Zheng, Jianfei Chen, Fan Bao, Jun Zhu |
| 2024 | Consistency Models for Scalable and Fast Simulation-Based Inference. Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev |
| 2024 | Consistency Purification: Effective and Efficient Diffusion Purification towards Certified Robustness. Yiquan Li, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Jiachen Lei, Bo Li, Chaowei Xiao |
| 2024 | Consistency of Neural Causal Partial Identification. Jiyuan Tan, Jose H. Blanchet, Vasilis Syrgkanis |
| 2024 | Constant Acceleration Flow. Dogyun Park, Sojin Lee, Sihyeon Kim, Taehoon Lee, Youngjoon Hong, Hyunwoo J. Kim |
| 2024 | Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data. Thibault Simonetto, Salah Ghamizi, Maxime Cordy |
| 2024 | Constrained Binary Decision Making. Daniel Prusa, Vojtech Franc |
| 2024 | Constrained Diffusion Models via Dual Training. Shervin Khalafi, Dongsheng Ding, Alejandro Ribeiro |
| 2024 | Constrained Diffusion with Trust Sampling. William Huang, Yifeng Jiang, Tom Van Wouwe, C. Karen Liu |
| 2024 | Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge. Weihua Du, Qiushi Lyu, Jiaming Shan, Zhenting Qi, Hongxin Zhang, Sunli Chen, Andi Peng, Tianmin Shu, Kwonjoon Lee, Behzad Dariush, Chuang Gan |
| 2024 | Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning. Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl |
| 2024 | Constrained Sampling with Primal-Dual Langevin Monte Carlo. Luiz F. O. Chamon, Mohammad Reza Karimi Jaghargh, Anna Korba |
| 2024 | Constrained Synthesis with Projected Diffusion Models. Jacob K. Christopher, Stephen Baek, Ferdinando Fioretto |
| 2024 | Constructing Semantics-Aware Adversarial Examples with a Probabilistic Perspective. Andi Zhang, Mingtian Zhang, Damon Wischik |
| 2024 | Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model. Yanpeng Ye, Jie Ren, Shaozhou Wang, Yuwei Wan, Imran Razzak, Bram Hoex, Haofen Wang, Tong Xie, Wenjie Zhang |
| 2024 | ContactField: Implicit Field Representation for Multi-Person Interaction Geometry. Hansol Lee, Tackgeun You, Hansoo Park, Woohyeon Shim, Sanghyeon Kim, Hwasup Lim |
| 2024 | Context and Geometry Aware Voxel Transformer for Semantic Scene Completion. Zhu Yu, Runmin Zhang, Jiacheng Ying, Junchen Yu, Xiaohai Hu, Lun Luo, Si-Yuan Cao, Hui-Liang Shen |
| 2024 | Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models. Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar |
| 2024 | ContextCite: Attributing Model Generation to Context. Benjamin Cohen-Wang, Harshay Shah, Kristian Georgiev, Aleksander Madry |
| 2024 | ContextGS : Compact 3D Gaussian Splatting with Anchor Level Context Model. Yufei Wang, Zhihao Li, Lanqing Guo, Wenhan Yang, Alex C. Kot, Bihan Wen |
| 2024 | Contextual Active Model Selection. Xuefeng Liu, Fangfang Xia, Rick Stevens, Yuxin Chen |
| 2024 | Contextual Bilevel Reinforcement Learning for Incentive Alignment. Vinzenz Thoma, Barna Pásztor, Andreas Krause, Giorgia Ramponi, Yifan Hu |
| 2024 | Contextual Decision-Making with Knapsacks Beyond the Worst Case. Zhaohua Chen, Rui Ai, Mingwei Yang, Yuqi Pan, Chang Wang, Xiaotie Deng |
| 2024 | Contextual Linear Optimization with Bandit Feedback. Yichun Hu, Nathan Kallus, Xiaojie Mao, Yanchen Wu |
| 2024 | Contextual Multinomial Logit Bandits with General Value Functions. Mengxiao Zhang, Haipeng Luo |
| 2024 | Continual Audio-Visual Sound Separation. Weiguo Pian, Yiyang Nan, Shijian Deng, Shentong Mo, Yunhui Guo, Yapeng Tian |
| 2024 | Continual Counting with Gradual Privacy Expiration. Joel Daniel Andersson, Monika Henzinger, Rasmus Pagh, Teresa Anna Steiner, Jalaj Upadhyay |
| 2024 | Continual Learning in the Frequency Domain. Ruiqi Liu, Boyu Diao, Libo Huang, Zijia An, Zhulin An, Yongjun Xu |
| 2024 | Continual Learning with Global Alignment. Xueying Bai, Jinghuan Shang, Yifan Sun, Niranjan Balasubramanian |
| 2024 | Continual learning with the neural tangent ensemble. Ari S. Benjamin, Christian-Gernot Pehle, Kyle Daruwalla |
| 2024 | Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition. Zi-Hao Zhou, Siyuan Fang, Zi-Jing Zhou, Tong Wei, Yuanyu Wan, Min-Ling Zhang |
| 2024 | Continuous Heatmap Regression for Pose Estimation via Implicit Neural Representation. Shengxiang Hu, Huaijiang Sun, Dong Wei, Xiaoning Sun, Jin Wang |
| 2024 | Continuous Partitioning for Graph-Based Semi-Supervised Learning. Chester Holtz, Pengwen Chen, Zhengchao Wan, Chung-Kuan Cheng, Gal Mishne |
| 2024 | Continuous Product Graph Neural Networks. Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo |
| 2024 | Continuous Spatiotemporal Events Decoupling through Spike-based Bayesian Computation. Yajing Zheng, Jiyuan Zhang, Zhaofei Yu, Tiejun Huang |
| 2024 | Continuous Temporal Domain Generalization. Zekun Cai, Guangji Bai, Renhe Jiang, Xuan Song, Liang Zhao |
| 2024 | Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving. Jianbiao Mei, Yukai Ma, Xuemeng Yang, Licheng Wen, Xinyu Cai, Xin Li, Daocheng Fu, Bo Zhang, Pinlong Cai, Min Dou, Botian Shi, Liang He, Yong Liu, Yu Qiao |
| 2024 | Contracting with a Learning Agent. Guru Guruganesh, Yoav Kolumbus, Jon Schneider, Inbal Talgam-Cohen, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Joshua R. Wang, S. Matthew Weinberg |
| 2024 | Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. Adriel Saporta, Aahlad Manas Puli, Mark Goldstein, Rajesh Ranganath |
| 2024 | Contrastive dimension reduction: when and how? Sam Hawke, YueEn Ma, Didong Li |
| 2024 | Contrastive losses as generalized models of global epistasis. David H. Brookes, Jakub Otwinowski, Sam Sinai |
| 2024 | Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT. Thomas E. Yerxa, Jenelle Feather, Eero P. Simoncelli, SueYeon Chung |
| 2024 | ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language Models. Mingrui Wu, Xinyue Cai, Jiayi Ji, Jiale Li, Oucheng Huang, Gen Luo, Hao Fei, Guannan Jiang, Xiaoshuai Sun, Rongrong Ji |
| 2024 | ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence. Wenjie Mei, Dongzhe Zheng, Shihua Li |
| 2024 | Controlled maximal variability along with reliable performance in recurrent neural networks. Chiara Mastrogiuseppe, Rubén Moreno-Bote |
| 2024 | Controlling Continuous Relaxation for Combinatorial Optimization. Yuma Ichikawa |
| 2024 | Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets. Eleni Straitouri, Suhas Thejaswi, Manuel Gomez Rodriguez |
| 2024 | Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound. Reuben Adams, John Shawe-Taylor, Benjamin Guedj |
| 2024 | ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Ablation Capability for Large Vision-Language Models. Shuo Liu, Kaining Ying, Hao Zhang, Yue Yang, Yuqi Lin, Tianle Zhang, Chuanhao Li, Yu Qiao, Ping Luo, Wenqi Shao, Kaipeng Zhang |
| 2024 | Convergence Analysis of Split Federated Learning on Heterogeneous Data. Pengchao Han, Chao Huang, Geng Tian, Ming Tang, Xin Liu |
| 2024 | Convergence of $\text{log}(1/\epsilon)$ for Gradient-Based Algorithms in Zero-Sum Games without the Condition Number: A Smoothed Analysis. Ioannis Anagnostides, Tuomas Sandholm |
| 2024 | Convergence of No-Swap-Regret Dynamics in Self-Play. Renato Paes Leme, Georgios Piliouras, Jon Schneider |
| 2024 | Convolutional Differentiable Logic Gate Networks. Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon |
| 2024 | Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods. Felix Dangel |
| 2024 | CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics. Jiawei Gao, Ziqin Wang, Zeqi Xiao, Jingbo Wang, Tai Wang, Jinkun Cao, Xiaolin Hu, Si Liu, Jifeng Dai, Jiangmiao Pang |
| 2024 | Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents. Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Schölkopf, Mrinmaya Sachan, Rada Mihalcea |
| 2024 | Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation. Sahar Abdelnabi, Amr Gomaa, Sarath Sivaprasad, Lea Schönherr, Mario Fritz |
| 2024 | Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging. Jiamian Wang, Zongliang Wu, Yulun Zhang, Xin Yuan, Tao Lin, Zhiqiang Tao |
| 2024 | Copycats: the many lives of a publicly available medical imaging dataset. Amelia Jiménez-Sánchez, Natalia Rozalia Avlona, Dovile Juodelyte, Théo Sourget, Caroline Vang-Larsen, Anna Rogers, Hubert Dariusz Zajac, Veronika Cheplygina |
| 2024 | CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning. Yibo Yang, Xiaojie Li, Zhongzhu Zhou, Shuaiwen Song, Jianlong Wu, Liqiang Nie, Bernard Ghanem |
| 2024 | Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification. Haolin Liu, Artin Tajdini, Andrew Wagenmaker, Chen-Yu Wei |
| 2024 | CosAE: Learnable Fourier Series for Image Restoration. Sifei Liu, Shalini De Mello, Jan Kautz |
| 2024 | Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index. Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, Alexander Terenin |
| 2024 | Cost-efficient Knowledge-based Question Answering with Large Language Models. Junnan Dong, Qinggang Zhang, Chuang Zhou, Hao Chen, Daochen Zha, Xiao Huang |
| 2024 | CountGD: Multi-Modal Open-World Counting. Niki Amini-Naieni, Tengda Han, Andrew Zisserman |
| 2024 | Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning. Chia-Hsiang Kao, Bharath Hariharan |
| 2024 | Counterfactual Fairness by Combining Factual and Counterfactual Predictions. Zeyu Zhou, Tianci Liu, Ruqi Bai, Jing Gao, Murat Kocaoglu, David I. Inouye |
| 2024 | Coupled Mamba: Enhanced Multimodal Fusion with Coupled State Space Model. Wenbing Li, Hang Zhou, Junqing Yu, Zikai Song, Wei Yang |
| 2024 | Covariate Shift Corrected Conditional Randomization Test. Bowen Xu, Yiwen Huang, Chuan Hong, Shuangning Li, Molei Liu |
| 2024 | Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions. Zhe Hu, Tuo Liang, Jing Li, Yiren Lu, Yunlai Zhou, Yiran Qiao, Jing Ma, Yu Yin |
| 2024 | Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions. Vinamra Benara, Chandan Singh, John X. Morris, Richard J. Antonello, Ion Stoica, Alexander Huth, Jianfeng Gao |
| 2024 | Credal Deep Ensembles for Uncertainty Quantification. Kaizheng Wang, Fabio Cuzzolin, Shireen Kudukkil Manchingal, Keivan Shariatmadar, David Moens, Hans Hallez |
| 2024 | Credal Learning Theory. Michele Caprio, Maryam Sultana, Eleni Elia, Fabio Cuzzolin |
| 2024 | Credit Attribution and Stable Compression. Roi Livni, Shay Moran, Kobbi Nissim, Chirag Pabbaraju |
| 2024 | CriticEval: Evaluating Large-scale Language Model as Critic. Tian Lan, Wenwei Zhang, Chen Xu, Heyan Huang, Dahua Lin, Kai Chen, Xian-Ling Mao |
| 2024 | Croissant: A Metadata Format for ML-Ready Datasets. Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Joan Giner-Miguelez, Pieter Gijsbers, Sujata S. Goswami, Nitisha Jain, Michalis Karamousadakis, Michael Kuchnik, Satyapriya Krishna, Sylvain Lesage, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Hamidah Oderinwale, Pierre Ruyssen, Tim Santos, Rajat Shinde, Elena Simperl, Arjun Suresh, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Susheel Varma, Jos van der Velde, Steffen Vogler, Carole-Jean Wu, Luyao Zhang |
| 2024 | Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias. Shan Chen, Jack Gallifant, Mingye Gao, Pedro Moreira, Nikolaj Munch, Ajay Muthukkumar, Arvind Rajan, Jaya Kolluri, Amelia Fiske, Janna Hastings, Hugo J. W. L. Aerts, Brian Anthony, Leo Anthony Celi, William G. La Cava, Danielle S. Bitterman |
| 2024 | Cross-Device Collaborative Test-Time Adaptation. Guohao Chen, Shuaicheng Niu, Deyu Chen, Shuhai Zhang, Changsheng Li, Yuanqing Li, Mingkui Tan |
| 2024 | Cross-Modality Perturbation Synergy Attack for Person Re-identification. Yunpeng Gong, Zhun Zhong, Yansong Qu, Zhiming Luo, Rongrong Ji, Min Jiang |
| 2024 | Cross-Scale Self-Supervised Blind Image Deblurring via Implicit Neural Representation. Tianjing Zhang, Yuhui Quan, Hui Ji |
| 2024 | Cross-modal Representation Flattening for Multi-modal Domain Generalization. Yunfeng Fan, Wenchao Xu, Haozhao Wang, Song Guo |
| 2024 | Cross-model Control: Improving Multiple Large Language Models in One-time Training. Jiayi Wu, Hao Sun, Hengyi Cai, Lixin Su, Shuaiqiang Wang, Dawei Yin, Xiang Li, Ming Gao |
| 2024 | Cross-video Identity Correlating for Person Re-identification Pre-training. Jialong Zuo, Ying Nie, Hanyu Zhou, Huaxin Zhang, Haoyu Wang, Tianyu Guo, Nong Sang, Changxin Gao |
| 2024 | CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM. Minkyu Jeon, Rishwanth Raghu, Miro Astore, Geoffrey Woollard, Ryan Feathers, Alkin Kaz, Sonya M. Hanson, Pilar Cossio, Ellen D. Zhong |
| 2024 | CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy. Jiakai Zhang, Qihe Chen, Yan Zeng, Wenyuan Gao, Xuming He, Zhijie Liu, Jingyi Yu |
| 2024 | CryoSPIN: Improving Ab-Initio Cryo-EM Reconstruction with Semi-Amortized Pose Inference. Shayan Shekarforoush, David B. Lindell, Marcus A. Brubaker, David J. Fleet |
| 2024 | Cryptographic Hardness of Score Estimation. Min Jae Song |
| 2024 | Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance. Kuan Heng Lin, Sicheng Mo, Ben Klingher, Fangzhou Mu, Bolei Zhou |
| 2024 | CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts. Jiachen Li, Xinyao Wang, Sijie Zhu, Chia-Wen Kuo, Lu Xu, Fan Chen, Jitesh Jain, Humphrey Shi, Longyin Wen |
| 2024 | CultureLLM: Incorporating Cultural Differences into Large Language Models. Cheng Li, Mengzhuo Chen, Jindong Wang, Sunayana Sitaram, Xing Xie |
| 2024 | CulturePark: Boosting Cross-cultural Understanding in Large Language Models. Cheng Li, Damien Teney, Linyi Yang, Qingsong Wen, Xing Xie, Jindong Wang |
| 2024 | Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise. Yeonguk Yu, Minhwan Ko, Sungho Shin, Kangmin Kim, Kyoobin Lee |
| 2024 | Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature. Deepak Ravikumar, Efstathia Soufleri, Kaushik Roy |
| 2024 | Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning. Jiawei Yao, Qi Qian, Juhua Hu |
| 2024 | Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction. Haotong Du, Quanming Yao, Juzheng Zhang, Yang Liu, Zhen Wang |
| 2024 | Customizing Language Models with Instance-wise LoRA for Sequential Recommendation. Xiaoyu Kong, Jiancan Wu, An Zhang, Leheng Sheng, Hui Lin, Xiang Wang, Xiangnan He |
| 2024 | CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns. Shengsheng Lin, Weiwei Lin, Xinyi Hu, Wentai Wu, Ruichao Mo, Haocheng Zhong |
| 2024 | D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models. Haoran Que, Jiaheng Liu, Ge Zhang, Chenchen Zhang, Xingwei Qu, Yinghao Ma, Feiyu Duan, Zhiqi Bai, Jiakai Wang, Yuanxing Zhang, Xu Tan, Jie Fu, Jiamang Wang, Lin Qu, Wenbo Su, Bo Zheng |
| 2024 | D-LLM: A Token Adaptive Computing Resource Allocation Strategy for Large Language Models. Yikun Jiang, Huanyu Wang, Lei Xie, Hanbin Zhao, Zhang Chao, Hui Qian, John C. S. Lui |
| 2024 | D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup. Joanna Waczynska, Piotr Borycki, Joanna Kaleta, Slawomir Konrad Tadeja, Przemyslaw Spurek |
| 2024 | D2R2: Diffusion-based Representation with Random Distance Matching for Tabular Few-shot Learning. Ruoxue Liu, Linjiajie Fang, Wenjia Wang, Bingyi Jing |
| 2024 | DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection. Haochen Li, Rui Zhang, Hantao Yao, Xin Zhang, Yifan Hao, Xinkai Song, Xiaqing Li, Yongwei Zhao, Yunji Chen, Ling Li |
| 2024 | DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation. Xueqing Wu, Rui Zheng, Jingzhen Sha, Te-Lin Wu, Hanyu Zhou, Mohan Tang, Kai-Wei Chang, Nanyun Peng, Haoran Huang |
| 2024 | DAGER: Exact Gradient Inversion for Large Language Models. Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev |
| 2024 | DALD: Improving Logits-based Detector without Logits from Black-box LLMs. Cong Zeng, Shengkun Tang, Xianjun Yang, Yuanzhou Chen, Yiyou Sun, Zhiqiang Xu, Yao Li, Haifeng Chen, Wei Cheng, Dongkuan Xu |
| 2024 | DAPE: Data-Adaptive Positional Encoding for Length Extrapolation. Chuanyang Zheng, Yihang Gao, Han Shi, Minbin Huang, Jingyao Li, Jing Xiong, Xiaozhe Ren, Michael K. Ng, Xin Jiang, Zhenguo Li, Yu Li |
| 2024 | DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph. Zhehao Zhang, Jiaao Chen, Diyi Yang |
| 2024 | DARNet: Dual Attention Refinement Network with Spatiotemporal Construction for Auditory Attention Detection. Sheng Yan, Cunhang Fan, Hongyu Zhang, Xiaoke Yang, Jianhua Tao, Zhao Lv |
| 2024 | DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA. Aman Patel, Arpita Singhal, Austin Wang, Anusri Pampari, Maya Kasowski, Anshul Kundaje |
| 2024 | DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving. Yuxuan Tong, Xiwen Zhang, Rui Wang, Ruidong Wu, Junxian He |
| 2024 | DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity. Baekrok Shin, Junsoo Oh, Hanseul Cho, Chulhee Yun |
| 2024 | DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain. Fengpeng Li, Kemou Li, Haiwei Wu, Jinyu Tian, Jiantao Zhou |
| 2024 | DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos. Linhan Wang, Kai Cheng, Shuo Lei, Shengkun Wang, Wei Yin, Chenyang Lei, Xiaoxiao Long, Chang-Tien Lu |
| 2024 | DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain. Kun Wang, Zhiqiang Yan, Junkai Fan, Wanlu Zhu, Xiang Li, Jun Li, Jian Yang |
| 2024 | DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering. Zhongpai Gao, Benjamin Planche, Meng Zheng, Xiao Chen, Terrence Chen, Ziyan Wu |
| 2024 | DDK: Distilling Domain Knowledge for Efficient Large Language Models. Jiaheng Liu, Chenchen Zhang, Jinyang Guo, Yuanxing Zhang, Haoran Que, Ken Deng, Zhiqi Bai, Jie Liu, Ge Zhang, Jiakai Wang, Yanan Wu, Congnan Liu, Jiamang Wang, Lin Qu, Wenbo Su, Bo Zheng |
| 2024 | DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting. Tao Dai, Beiliang Wu, Peiyuan Liu, Naiqi Li, Xue Yuerong, Shu-Tao Xia, Zexuan Zhu |
| 2024 | DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor. Juncheng Wu, Zhangkai Ni, Hanli Wang, Wenhan Yang, Yuyin Zhou, Shiqi Wang |
| 2024 | DECO-Bench: Unified Benchmark for Decoupled Task-Agnostic Synthetic Data Release. Farzaneh Askari, Lingjuan Lyu, Vivek Sharma |
| 2024 | DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach. Qian Chen, Ling Chen |
| 2024 | DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Riccardo Barbano, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lió |
| 2024 | DEL: Discrete Element Learner for Learning 3D Particle Dynamics with Neural Rendering. Jiaxu Wang, Jingkai Sun, Ziyi Zhang, Junhao He, Qiang Zhang, Mingyuan Sun, Renjing Xu |
| 2024 | DEPrune: Depth-wise Separable Convolution Pruning for Maximizing GPU Parallelism. Cheonjun Park, Mincheol Park, Hyunchan Moon, Myung Kuk Yoon, Seokjin Go, Suhyun Kim, Won Woo Ro |
| 2024 | DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning. Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low |
| 2024 | DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators. Taesik Gong, Fahim Kawsar, Chulhong Min |
| 2024 | DF40: Toward Next-Generation Deepfake Detection. Zhiyuan Yan, Taiping Yao, Shen Chen, Yandan Zhao, Xinghe Fu, Junwei Zhu, Donghao Luo, Chengjie Wang, Shouhong Ding, Yunsheng Wu, Li Yuan |
| 2024 | DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment. Gongpei Zhao, Tao Wang, Congyan Lang, Yi Jin, Yidong Li, Haibin Ling |
| 2024 | DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization. Yueming Xu, Haochen Jiang, Zhongyang Xiao, Jianfeng Feng, Li Zhang |
| 2024 | DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion. Yilong Chen, Linhao Zhang, Junyuan Shang, Zhenyu Zhang, Tingwen Liu, Shuohuan Wang, Yu Sun |
| 2024 | DI-MaskDINO: A Joint Object Detection and Instance Segmentation Model. Zhixiong Nan, Xianghong Li, Tao Xiang, Jifeng Dai |
| 2024 | DINTR: Tracking via Diffusion-based Interpolation. Pha A. Nguyen, Ngan Le, Jackson David Cothren, Alper Yilmaz, Khoa Luu |
| 2024 | DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models. Shangqian Gao, Chi-Heng Lin, Ting Hua, Zheng Tang, Yilin Shen, Hongxia Jin, Yen-Chang Hsu |
| 2024 | DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors. Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach, J. Swaroop Guntupalli, Carter Wendelken, Ahmad Humayun, Sivaramakrishnan Swaminathan, Guangyao Zhou, Miguel Lázaro-Gredilla, Kevin P. Murphy |
| 2024 | DMNet: Self-comparison Driven Model for Subject-independent Seizure Detection. Shihao Tu, Linfeng Cao, Daoze Zhang, Junru Chen, Lvbin Ma, Yin Zhang, Yang Yang |
| 2024 | DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models. Hengkang Wang, Xu Zhang, Taihui Li, Yuxiang Wan, Tiancong Chen, Ju Sun |
| 2024 | DMesh: A Differentiable Mesh Representation. Sanghyun Son, Matheus Gadelha, Yang Zhou, Zexiang Xu, Ming C. Lin, Yi Zhou |
| 2024 | DN-4DGS: Denoised Deformable Network with Temporal-Spatial Aggregation for Dynamic Scene Rendering. Jiahao Lu, Jiacheng Deng, Ruijie Zhu, Yanzhe Liang, Wenfei Yang, Xu Zhou, Tianzhu Zhang |
| 2024 | DOFEN: Deep Oblivious Forest ENsemble. Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, Tien-Hao Chang |
| 2024 | DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus. Yu Chen, Gim Hee Lee |
| 2024 | DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction. Xinwei Zhang, Zhiqi Bu, Mingyi Hong, Meisam Razaviyayn |
| 2024 | DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection. Xiao Yu, Yuang Qi, Kejiang Chen, Guoqiang Chen, Xi Yang, Pengyuan Zhu, Xiuwei Shang, Weiming Zhang, Nenghai Yu |
| 2024 | DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM. Yingjun Shen, Haizhao Dai, Qihe Chen, Yan Zeng, Jiakai Zhang, Yuan Pei, Jingyi Yu |
| 2024 | DRIP: Unleashing Diffusion Priors for Joint Foreground and Alpha Prediction in Image Matting. Xiaodi Li, Zongxin Yang, Ruijie Quan, Yi Yang |
| 2024 | DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs. Jiasheng Zhang, Jialin Chen, Menglin Yang, Aosong Feng, Shuang Liang, Jie Shao, Rex Ying |
| 2024 | DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation. Felipe Garrido-Lucero, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet |
| 2024 | DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices. Yongzhe Jia, Xuyun Zhang, Hongsheng Hu, Kim-Kwang Raymond Choo, Lianyong Qi, Xiaolong Xu, Amin Beheshti, Wanchun Dou |
| 2024 | DarkSAM: Fooling Segment Anything Model to Segment Nothing. Ziqi Zhou, Yufei Song, Minghui Li, Shengshan Hu, Xianlong Wang, Leo Yu Zhang, Dezhong Yao, Hai Jin |
| 2024 | Data Acquisition via Experimental Design for Data Markets. Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael I. Jordan, Ramesh Raskar |
| 2024 | Data Attribution for Text-to-Image Models by Unlearning Synthesized Images. Sheng-Yu Wang, Aaron Hertzmann, Alexei A. Efros, Jun-Yan Zhu, Richard Zhang |
| 2024 | Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning. Seonghyun Ban, Heesan Kong, Kee-Eung Kim |
| 2024 | Data Distribution Valuation. Xinyi Xu, Shuaiqi Wang, Chuan Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti |
| 2024 | Data Free Backdoor Attacks. Bochuan Cao, Jinyuan Jia, Chuxuan Hu, Wenbo Guo, Zhen Xiang, Jinghui Chen, Bo Li, Dawn Song |
| 2024 | Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions. Jonathan Hayase, Alisa Liu, Yejin Choi, Sewoong Oh, Noah A. Smith |
| 2024 | Data curation via joint example selection further accelerates multimodal learning. Talfan Evans, Nikhil Parthasarathy, Hamza Merzic, Olivier J. Hénaff |
| 2024 | Data subsampling for Poisson regression with pth-root-link. Han Cheng Lie, Alexander Munteanu |
| 2024 | Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models. Samuel Holt, Zhaozhi Qian, Tennison Liu, James Weatherall, Mihaela van der Schaar |
| 2024 | Data-Efficient Learning with Neural Programs. Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong |
| 2024 | Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning. Wuyang Chen, Jialin Song, Pu Ren, Shashank Subramanian, Dmitriy Morozov, Michael W. Mahoney |
| 2024 | Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables. Qiheng Sun, Haocheng Xia, Jinfei Liu |
| 2024 | DataComp-LM: In search of the next generation of training sets for language models. Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick Scott Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee F. Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alex Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar |
| 2024 | DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans. Yuan Gan, Jiaxu Miao, Yi Yang |
| 2024 | Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum. Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Oncel Tuzel |
| 2024 | Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition. Edoardo Debenedetti, Javier Rando, Daniel Paleka, Silaghi Fineas Florin, Dragos Albastroiu, Niv Cohen, Yuval Lemberg, Reshmi Ghosh, Rui Wen, Ahmed Salem, Giovanni Cherubin, Santiago Zanella-Béguelin, Robin Schmid, Victor Klemm, Takahiro Miki, Chenhao Li, Stefan Kraft, Mario Fritz, Florian Tramèr, Sahar Abdelnabi, Lea Schönherr |
| 2024 | DeBaRA: Denoising-Based 3D Room Arrangement Generation. Léopold Maillard, Nicolas Sereyjol-Garros, Tom Durand, Maks Ovsjanikov |
| 2024 | DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States. Bozhou Zhang, Nan Song, Li Zhang |
| 2024 | DeNetDM: Debiasing by Network Depth Modulation. Silpa Vadakkeeveetil Sreelatha, Adarsh Kappiyath, Abhra Chaudhuri, Anjan Dutta |
| 2024 | DePLM: Denoising Protein Language Models for Property Optimization. Zeyuan Wang, Keyan Ding, Ming Qin, Xiaotong Li, Xiang Zhuang, Yu Zhao, Jianhua Yao, Qiang Zhang, Huajun Chen |
| 2024 | DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms. Oryan Yehezkel, Alon Zolfi, Amit Baras, Yuval Elovici, Asaf Shabtai |
| 2024 | DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning. Xun Guo, Yongxin He, Shan Zhang, Ting Zhang, Wanquan Feng, Haibin Huang, Chongyang Ma |
| 2024 | DeTikZify: Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ. Jonas Belouadi, Simone Paolo Ponzetto, Steffen Eger |
| 2024 | DeTrack: In-model Latent Denoising Learning for Visual Object Tracking. Xinyu Zhou, Jinglun Li, Lingyi Hong, Kaixun Jiang, Pinxue Guo, Weifeng Ge, Wenqiang Zhang |
| 2024 | Dealing with Synthetic Data Contamination in Online Continual Learning. Maorong Wang, Nicolas Michel, Jiafeng Mao, Toshihiko Yamasaki |
| 2024 | Debiasing Synthetic Data Generated by Deep Generative Models. Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Johan Decruyenaere, Christiaan Polet, Thomas Demeester, Stijn Vansteelandt |
| 2024 | Decentralized Noncooperative Games with Coupled Decision-Dependent Distributions. Wenjing Yan, Xuanyu Cao |
| 2024 | Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL. Qi Lv, Xiang Deng, Gongwei Chen, Michael Yu Wang, Liqiang Nie |
| 2024 | Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling. Sili Huang, Jifeng Hu, Zhejian Yang, Liwei Yang, Tao Luo, Hechang Chen, Lichao Sun, Bo Yang |
| 2024 | Decision-Focused Learning with Directional Gradients. Michael Huang, Vishal Gupta |
| 2024 | Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context. Jingru Jia, Zehua Yuan, Junhao Pan, Paul McNamara, Deming Chen |
| 2024 | Decoding-Time Language Model Alignment with Multiple Objectives. Ruizhe Shi, Yifang Chen, Yushi Hu, Alisa Liu, Hanna Hajishirzi, Noah A. Smith, Simon S. Du |
| 2024 | Decomposable Transformer Point Processes. Aristeidis Panos |
| 2024 | Decompose, Analyze and Rethink: Solving Intricate Problems with Human-like Reasoning Cycle. Shangzi Xue, Zhenya Huang, Jiayu Liu, Xin Lin, Yuting Ning, Binbin Jin, Xin Li, Qi Liu |
| 2024 | Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization. Hongling Zheng, Li Shen, Yong Luo, Tongliang Liu, Jialie Shen, Dacheng Tao |
| 2024 | Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIP. Sriram Balasubramanian, Samyadeep Basu, Soheil Feizi |
| 2024 | Decoupled Kullback-Leibler Divergence Loss. Jiequan Cui, Zhuotao Tian, Zhisheng Zhong, Xiaojuan Qi, Bei Yu, Hanwang Zhang |
| 2024 | Decoupling Semantic Similarity from Spatial Alignment for Neural Networks. Tassilo Wald, Constantin Ulrich, Priyank Jaini, Gregor Köhler, David Zimmerer, Stefan Denner, Fabian Isensee, Michael Baumgartner, Klaus H. Maier-Hein |
| 2024 | DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution. Yang Yue, Yulin Wang, Bingyi Kang, Yizeng Han, Shenzhi Wang, Shiji Song, Jiashi Feng, Gao Huang |
| 2024 | Deep Bayesian Active Learning for Preference Modeling in Large Language Models. Luckeciano Carvalho Melo, Panagiotis Tigas, Alessandro Abate, Yarin Gal |
| 2024 | Deep Correlated Prompting for Visual Recognition with Missing Modalities. Lianyu Hu, Tongkai Shi, Wei Feng, Fanhua Shang, Liang Wan |
| 2024 | Deep Equilibrium Algorithmic Reasoning. Dobrik Georgiev, Joseph Wilson, Davide Buffelli, Pietro Lió |
| 2024 | Deep Graph Mating. Yongcheng Jing, Seok-Hee Hong, Dacheng Tao |
| 2024 | Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module. Jingbo Zhou, Yixuan Du, Ruqiong Zhang, Jun Xia, Zhizhi Yu, Zelin Zang, Di Jin, Carl Yang, Rui Zhang, Stan Z. Li |
| 2024 | Deep Homomorphism Networks. Takanori Maehara, Hoang NT |
| 2024 | Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. Alan Jeffares, Alicia Curth, Mihaela van der Schaar |
| 2024 | Deep Learning for Computing Convergence Rates of Markov Chains. Yanlin Qu, Jose H. Blanchet, Peter W. Glynn |
| 2024 | Deep Learning in Medical Image Registration: Magic or Mirage? Rohit Jena, Deeksha Sethi, Pratik Chaudhari, James C. Gee |
| 2024 | Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers. Gautham Vasan, Mohamed Elsayed, Seyed Alireza Azimi, Jiamin He, Fahim Shahriar, Colin Bellinger, Martha White, Rupam Mahmood |
| 2024 | Deep Submodular Peripteral Networks. Gantavya Bhatt, Arnav Das, Jeff A. Bilmes |
| 2024 | Deep Support Vectors. JunHoo Lee, Hyunho Lee, Kyomin Hwang, Nojun Kwak |
| 2024 | Deep linear networks for regression are implicitly regularized towards flat minima. Pierre Marion, Lénaïc Chizat |
| 2024 | DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection. Hongyu Shen, Yici Yan, Zhizhen Jane Zhao |
| 2024 | DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation. Hongyuan Tao, Hang Yu, Jianguo Li |
| 2024 | DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction. Qilong Ma, Haixu Wu, Lanxiang Xing, Shangchen Miao, Mingsheng Long |
| 2024 | DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs. Lingchen Meng, Jianwei Yang, Rui Tian, Xiyang Dai, Zuxuan Wu, Jianfeng Gao, Yu-Gang Jiang |
| 2024 | Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models. Yimeng Zhang, Xin Chen, Jinghan Jia, Yihua Zhang, Chongyu Fan, Jiancheng Liu, Mingyi Hong, Ke Ding, Sijia Liu |
| 2024 | DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching. Donghao Luo, Xue Wang |
| 2024 | DeiSAM: Segment Anything with Deictic Prompting. Hikaru Shindo, Manuel Brack, Gopika Sudhakaran, Devendra Singh Dhami, Patrick Schramowski, Kristian Kersting |
| 2024 | Delta-CoMe: Training-Free Delta-Compression with Mixed-Precision for Large Language Models. Bowen Ping, Shuo Wang, Hanqing Wang, Xu Han, Yuzhuang Xu, Yukun Yan, Yun Chen, Baobao Chang, Zhiyuan Liu, Maosong Sun |
| 2024 | DeltaDEQ: Exploiting Heterogeneous Convergence for Accelerating Deep Equilibrium Iterations. Zuowen Wang, Longbiao Cheng, Pehuen Moure, Niklas Hahn, Shih-Chii Liu |
| 2024 | DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking. Jiaxian Yan, Zaixi Zhang, Jintao Zhu, Kai Zhang, Jianfeng Pei, Qi Liu |
| 2024 | Delving into the Reversal Curse: How Far Can Large Language Models Generalize? Zhengkai Lin, Zhihang Fu, Kai Liu, Liang Xie, Binbin Lin, Wenxiao Wang, Deng Cai, Yue Wu, Jieping Ye |
| 2024 | Demystify Mamba in Vision: A Linear Attention Perspective. Dongchen Han, Ziyi Wang, Zhuofan Xia, Yizeng Han, Yifan Pu, Chunjiang Ge, Jun Song, Shiji Song, Bo Zheng, Gao Huang |
| 2024 | Dendritic Integration Inspired Artificial Neural Networks Capture Data Correlation. Chongming Liu, Jingyang Ma, Songting Li, Douglas Zhou |
| 2024 | DenoiseRep: Denoising Model for Representation Learning. Zhengrui Xu, Guan'an Wang, Xiaowen Huang, Jitao Sang |
| 2024 | Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model. Yiming Lei, Zilong Li, Junping Zhang, Hongming Shan |
| 2024 | Dense Associative Memory Through the Lens of Random Features. Benjamin Hoover, Duen Horng Chau, Hendrik Strobelt, Parikshit Ram, Dmitry Krotov |
| 2024 | Dense Connector for MLLMs. Huanjin Yao, Wenhao Wu, Taojiannan Yang, Yuxin Song, Mengxi Zhang, Haocheng Feng, Yifan Sun, Zhiheng Li, Wanli Ouyang, Jingdong Wang |
| 2024 | DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging. Matteo Pagliardini, Amirkeivan Mohtashami, François Fleuret, Martin Jaggi |
| 2024 | DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception. Xiaotong Li, Fan Zhang, Haiwen Diao, Yueze Wang, Xinlong Wang, Lingyu Duan |
| 2024 | Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval. Haolun Wu, Ofer Meshi, Masrour Zoghi, Fernando Diaz, Xue (Steve) Liu, Craig Boutilier, Maryam Karimzadehgan |
| 2024 | Depth Anything V2. Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao |
| 2024 | Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation. Ning-Hsu Wang, Yu-Lun Liu |
| 2024 | Derandomizing Multi-Distribution Learning. Kasper Green Larsen, Omar Montasser, Nikita Zhivotovskiy |
| 2024 | Derivative-enhanced Deep Operator Network. Yuan Qiu, Nolan Bridges, Peng Chen |
| 2024 | Derivatives of Stochastic Gradient Descent in parametric optimization. Franck Iutzeler, Edouard Pauwels, Samuel Vaiter |
| 2024 | Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization. Aniketh Janardhan Reddy, Xinyang Geng, Michael Herschl, Sathvik Kolli, Aviral Kumar, Patrick Hsu, Sergey Levine, Nilah Ioannidis |
| 2024 | Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems. Rohan R. Paleja, Michael Munje, Kimberlee Chestnut Chang, Reed Jensen, Matthew C. Gombolay |
| 2024 | DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios. Junchao Wu, Runzhe Zhan, Derek F. Wong, Shu Yang, Xinyi Yang, Yulin Yuan, Lidia S. Chao |
| 2024 | Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers. Jonas Ngnawé, Sabyasachi Sahoo, Yann Pequignot, Frédéric Precioso, Christian Gagné |
| 2024 | Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning. Brian Zhang, Zhuo Zhang |
| 2024 | Detecting and Measuring Confounding Using Causal Mechanism Shifts. Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian |
| 2024 | Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time. Jeremy McMahan |
| 2024 | Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. Abdullah Akgül, Manuel Haussmann, Melih Kandemir |
| 2024 | DevBench: A multimodal developmental benchmark for language learning. Alvin Wei Ming Tan, Chunhua yu, Bria Long, Wanjing Ma, Tonya Murray, Rebecca D. Silverman, Jason D. Yeatman, Michael C. Frank |
| 2024 | DiGRAF: Diffeomorphic Graph-Adaptive Activation Function. Krishna Sri Ipsit Mantri, Xinzhi Wang, Carola-Bibiane Schönlieb, Bruno Ribeiro, Beatrice Bevilacqua, Moshe Eliasof |
| 2024 | DiMSUM: Diffusion Mamba - A Scalable and Unified Spatial-Frequency Method for Image Generation. Hao Phung, Quan Dao, Trung Tuan Dao, Viet Hoang Phan, Dimitris N. Metaxas, Anh Tuan Tran |
| 2024 | DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization. Haowei Zhu, Dehua Tang, Ji Liu, Mingjie Lu, Jintu Zheng, Jinzhang Peng, Dong Li, Yu Wang, Fan Jiang, Lu Tian, Spandan Tiwari, Ashish Sirasao, Jun-Hai Yong, Bin Wang, Emad Barsoum |
| 2024 | DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection. Jia Syuen Lim, Zhuoxiao Chen, Zhi Chen, Mahsa Baktashmotlagh, Xin Yu, Zi Huang, Yadan Luo |
| 2024 | DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models. Bowen Wang, Jiuyang Chang, Yiming Qian, Guoxin Chen, Junhao Chen, Zhouqiang Jiang, Jiahao Zhang, Yuta Nakashima, Hajime Nagahara |
| 2024 | DiTFastAttn: Attention Compression for Diffusion Transformer Models. Zhihang Yuan, Hanling Zhang, Lu Pu, Xuefei Ning, Linfeng Zhang, Tianchen Zhao, Shengen Yan, Guohao Dai, Yu Wang |
| 2024 | Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models. Lai Wei, Zhiquan Tan, Chenghai Li, Jindong Wang, Weiran Huang |
| 2024 | DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers. Chandramouli Shama Sastry, Sri Harsha Dumpala, Sageev Oore |
| 2024 | DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut. Paul Couairon, Mustafa Shukor, Jean-Emmanuel Haugeard, Matthieu Cord, Nicolas Thome |
| 2024 | DiffGS: Functional Gaussian Splatting Diffusion. Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu |
| 2024 | DiffHammer: Rethinking the Robustness of Diffusion-Based Adversarial Purification. Kaibo Wang, Xiaowen Fu, Yuxuan Han, Yang Xiang |
| 2024 | DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data. Hanyang Chen, Yang Jiang, Shengnan Guo, Xiaowei Mao, Youfang Lin, Huaiyu Wan |
| 2024 | DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation. Weiting Tan, Jingyu Zhang, Lingfeng Shen, Daniel Khashabi, Philipp Koehn |
| 2024 | DiffPO: A causal diffusion model for learning distributions of potential outcomes. Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal, Stefan Feuerriegel |
| 2024 | DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion. Weicai Ye, Chenhao Ji, Zheng Chen, Junyao Gao, Xiaoshui Huang, Song-Hai Zhang, Wanli Ouyang, Tong He, Cairong Zhao, Guofeng Zhang |
| 2024 | DiffPhyCon: A Generative Approach to Control Complex Physical Systems. Long Wei, Peiyan Hu, Ruiqi Feng, Haodong Feng, Yixuan Du, Tao Zhang, Rui Wang, Yue Wang, Zhi-Ming Ma, Tailin Wu |
| 2024 | DiffSF: Diffusion Models for Scene Flow Estimation. Yushan Zhang, Bastian Wandt, Maria Magnusson, Michael Felsberg |
| 2024 | DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning. Weikang Wan, Ziyu Wang, Yufei Wang, Zackory Erickson, David Held |
| 2024 | Diffeomorphic interpolation for efficient persistence-based topological optimization. Mathieu Carrière, Marc Theveneau, Théo Lacombe |
| 2024 | Differentiable Modal Synthesis for Physical Modeling of Planar String Sound and Motion Simulation. Jin Woo Lee, Jaehyun Park, Min Jun Choi, Kyogu Lee |
| 2024 | Differentiable Quantum Computing for Large-scale Linear Control. Connor Clayton, Jiaqi Leng, Gengzhi Yang, Yi-Ling Qiao, Ming C. Lin, Xiaodi Wu |
| 2024 | Differentiable Structure Learning with Partial Orders. Taiyu Ban, Lyuzhou Chen, Xiangyu Wang, Xin Wang, Derui Lyu, Huanhuan Chen |
| 2024 | Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos. Luigi Seminara, Giovanni Maria Farinella, Antonino Furnari |
| 2024 | Differential Privacy in Scalable General Kernel Learning via $K$-means Nystr{\"o}m Random Features. Bonwoo Lee, Jeongyoun Ahn, Cheolwoo Park |
| 2024 | Differentially Private Equivalence Testing for Continuous Distributions and Applications. Or Sheffet, Daniel Omer |
| 2024 | Differentially Private Graph Diffusion with Applications in Personalized PageRanks. Rongzhe Wei, Eli Chien, Pan Li |
| 2024 | Differentially Private Optimization with Sparse Gradients. Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi |
| 2024 | Differentially Private Reinforcement Learning with Self-Play. Dan Qiao, Yu-Xiang Wang |
| 2024 | Differentially Private Set Representations. Sarvar Patel, Giuseppe Persiano, Joon Young Seo, Kevin Yeo |
| 2024 | Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement. Jeremiah Birrell, Reza Ebrahimi, Rouzbeh Behnia, Jason Pacheco |
| 2024 | DiffuBox: Refining 3D Object Detection with Point Diffusion. Xiangyu Chen, Zhenzhen Liu, Katie Luo, Siddhartha Datta, Adhitya Polavaram, Yan Wang, Yurong You, Boyi Li, Marco Pavone, Wei-Lun Chao, Mark E. Campbell, Bharath Hariharan, Kilian Q. Weinberger |
| 2024 | DiffuLT: Diffusion for Long-tail Recognition Without External Knowledge. Jie Shao, Ke Zhu, Hanxiao Zhang, Jianxin Wu |
| 2024 | DiffuPac: Contextual Mimicry in Adversarial Packets Generation via Diffusion Model. Abdullah Bin Jasni, Akiko Manada, Kohei Watabe |
| 2024 | DiffuserLite: Towards Real-time Diffusion Planning. Zibin Dong, Jianye Hao, Yifu Yuan, Fei Ni, Yitian Wang, Pengyi Li, Yan Zheng |
| 2024 | Diffusing Differentiable Representations. Yash Savani, Marc Finzi, J. Zico Kolter |
| 2024 | Diffusion Actor-Critic with Entropy Regulator. Yinuo Wang, Likun Wang, Yuxuan Jiang, Wenjun Zou, Tong Liu, Xujie Song, Wenxuan Wang, Liming Xiao, Jiang Wu, Jingliang Duan, Shengbo Li |
| 2024 | Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion. Boyuan Chen, Diego Marti Monso, Yilun Du, Max Simchowitz, Russ Tedrake, Vincent Sitzmann |
| 2024 | Diffusion Imitation from Observation. Bo-Ruei Huang, Chun-Kai Yang, Chun-Mao Lai, Dai-Jie Wu, Shao-Hua Sun |
| 2024 | Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement. Tao Yang, Cuiling Lan, Yan Lu, Nanning Zheng |
| 2024 | Diffusion Models With Learned Adaptive Noise. Subham S. Sahoo, Aaron Gokaslan, Christopher De Sa, Volodymyr Kuleshov |
| 2024 | Diffusion Models are Certifiably Robust Classifiers. Huanran Chen, Yinpeng Dong, Shitong Shao, Zhongkai Hao, Xiao Yang, Hang Su, Jun Zhu |
| 2024 | Diffusion PID: Interpreting Diffusion via Partial Information Decomposition. Shaurya Dewan, Rushikesh Zawar, Prakanshul Saxena, Yingshan Chang, Andrew F. Luo, Yonatan Bisk |
| 2024 | Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning. Tianyu Chen, Zhendong Wang, Mingyuan Zhou |
| 2024 | Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies. Yipu Chen, Haotian Xue, Yongxin Chen |
| 2024 | Diffusion Priors for Variational Likelihood Estimation and Image Denoising. Jun Cheng, Shan Tan |
| 2024 | Diffusion Spectral Representation for Reinforcement Learning. Dmitry Shribak, Chen-Xiao Gao, Yitong Li, Chenjun Xiao, Bo Dai |
| 2024 | Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting. Jincheng Zhong, Xingzhuo Guo, Jiaxiang Dong, Mingsheng Long |
| 2024 | Diffusion Twigs with Loop Guidance for Conditional Graph Generation. Giangiacomo Mercatali, Yogesh Verma, André Freitas, Vikas Garg |
| 2024 | Diffusion for World Modeling: Visual Details Matter in Atari. Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos J. Storkey, Tim Pearce, François Fleuret |
| 2024 | Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models. Jiacheng Ye, Shansan Gong, Liheng Chen, Lin Zheng, Jiahui Gao, Han Shi, Chuan Wu, Xin Jiang, Zhenguo Li, Wei Bi, Lingpeng Kong |
| 2024 | Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning. Liyuan Mao, Haoran Xu, Xianyuan Zhan, Weinan Zhang, Amy Zhang |
| 2024 | Diffusion-Inspired Truncated Sampler for Text-Video Retrieval. Jiamian Wang, Pichao Wang, Dongfang Liu, Qiang Guan, Sohail A. Dianat, Majid Rabbani, Raghuveer Rao, Zhiqiang Tao |
| 2024 | Diffusion-Reward Adversarial Imitation Learning. Chun-Mao Lai, Hsiang-Chun Wang, Ping-Chun Hsieh, Yu-Chiang Frank Wang, Min-Hung Chen, Shao-Hua Sun |
| 2024 | Diffusion-based Curriculum Reinforcement Learning. Erdi Sayar, Giovanni Iacca, Ozgur S. Oguz, Alois Knoll |
| 2024 | Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection. Ying Yang, De Cheng, Chaowei Fang, Yubiao Wang, Changzhe Jiao, Lechao Cheng, Nannan Wang, Xinbo Gao |
| 2024 | Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization. Shutong Ding, Ke Hu, Zhenhao Zhang, Kan Ren, Weinan Zhang, Jingyi Yu, Jingya Wang, Ye Shi |
| 2024 | Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models. Hanwen Liang, YuYang Yin, Dejia Xu, Hanxue Liang, Zhangyang Wang, Konstantinos N. Plataniotis, Yao Zhao, Yunchao Wei |
| 2024 | DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstruction. Bowen Song, Jason Hu, Zhaoxu Luo, Jeffrey A. Fessler, Liyue Shen |
| 2024 | DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion. Ke Sun, Shen Chen, Taiping Yao, Hong Liu, Xiaoshuai Sun, Shouhong Ding, Rongrong Ji |
| 2024 | DiffusionPDE: Generative PDE-Solving under Partial Observation. Jiahe Huang, Guandao Yang, Zichen Wang, Jeong Joon Park |
| 2024 | DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning. Hao Bai, Yifei Zhou, Jiayi Pan, Mert Cemri, Alane Suhr, Sergey Levine, Aviral Kumar |
| 2024 | Dimension-free Private Mean Estimation for Anisotropic Distributions. Yuval Dagan, Michael I. Jordan, Xuelin Yang, Lydia Zakynthinou, Nikita Zhivotovskiy |
| 2024 | Dimension-free deterministic equivalents and scaling laws for random feature regression. Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz |
| 2024 | Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion models. Kyungmin Lee, Sangkyung Kwak, Kihyuk Sohn, Jinwoo Shin |
| 2024 | Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits. Tian Huang, Shengbo Wang, Ke Li |
| 2024 | Direct Unlearning Optimization for Robust and Safe Text-to-Image Models. Yong-Hyun Park, Sangdoo Yun, Jin-Hwa Kim, Junho Kim, Geonhui Jang, Yonghyun Jeong, Junghyo Jo, Gayoung Lee |
| 2024 | Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer. Shuang Wu, Youtian Lin, Yifei Zeng, Feihu Zhang, Jingxi Xu, Philip Torr, Xun Cao, Yao Yao |
| 2024 | Directional Smoothness and Gradient Methods: Convergence and Adaptivity. Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower |
| 2024 | Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text. Xinyang Li, Zhangyu Lai, Linning Xu, Yansong Qu, Liujuan Cao, Shengchuan Zhang, Bo Dai, Rongrong Ji |
| 2024 | DisC-GS: Discontinuity-aware Gaussian Splatting. Haoxuan Qu, Zhuoling Li, Hossein Rahmani, Yujun Cai, Jun Liu |
| 2024 | DisCEdit: Model Editing by Identifying Discriminative Components. Chaitanya Murti, Chiranjib Bhattacharyya |
| 2024 | Discovering Creative Behaviors through DUPLEX: Diverse Universal Features for Policy Exploration. Borja G. León, Francesco Riccio, Kaushik Subramanian, Peter R. Wurman, Peter Stone |
| 2024 | Discovering Preference Optimization Algorithms with and for Large Language Models. Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob N. Foerster, Mihaela van der Schaar, Robert T. Lange |
| 2024 | Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models. Lujun Li, Peijie Dong, Zhenheng Tang, Xiang Liu, Qiang Wang, Wenhan Luo, Wei Xue, Qifeng Liu, Xiaowen Chu, Yike Guo |
| 2024 | Discovering plasticity rules that organize and maintain neural circuits. David Bell, Alison Duffy, Adrienne Fairhall |
| 2024 | Discovery of the Hidden World with Large Language Models. Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang |
| 2024 | DiscoveryWorld: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents. Peter A. Jansen, Marc-Alexandre Côté, Tushar Khot, Erin Bransom, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Oyvind Tafjord, Peter Clark |
| 2024 | Discrete Dictionary-based Decomposition Layer for Structured Representation Learning. Taewon Park, Hyun-Chul Kim, Minho Lee |
| 2024 | Discrete Flow Matching. Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman |
| 2024 | Discrete Modeling via Boundary Conditional Diffusion Processes. Yuxuan Gu, Xiaocheng Feng, Lei Huang, Yingsheng Wu, Zekun Zhou, Weihong Zhong, Kun Zhu, Bing Qin |
| 2024 | Discrete-state Continuous-time Diffusion for Graph Generation. Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Menghai Pan, Zhichen Zeng, Mahashweta Das, Hanghang Tong |
| 2024 | Discretely beyond 1/e: Guided Combinatorial Algortihms for Submodular Maximization. Yixin Chen, Ankur Nath, Chunli Peng, Alan Kuhnle |
| 2024 | DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis. Shangshang Yang, Mingyang Chen, Ziwen Wang, Xiaoshan Yu, Panpan Zhang, Haiping Ma, Xingyi Zhang |
| 2024 | Disentangled Representation Learning in Non-Markovian Causal Systems. Adam Li, Yushu Pan, Elias Bareinboim |
| 2024 | Disentangled Style Domain for Implicit z-Watermark Towards Copyright Protection. Junqiang Huang, Zhaojun Guo, Ge Luo, Zhenxing Qian, Sheng Li, Xinpeng Zhang |
| 2024 | Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning. Jiaheng Hu, Zizhao Wang, Peter Stone, Roberto Martín-Martín |
| 2024 | Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis. Jiayu Su, David A. Knowles, Raúl Rabadán |
| 2024 | Disentangling Linear Quadratic Control with Untrusted ML Predictions. Tongxin Li, Hao Liu, Yisong Yue |
| 2024 | Disentangling and mitigating the impact of task similarity for continual learning. Naoki Hiratani |
| 2024 | Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems. Aditi Jha, Diksha Gupta, Carlos D. Brody, Jonathan W. Pillow |
| 2024 | Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation. Juan Claude Formanek, Callum Rhys Tilbury, Louise Beyers, Jonathan Shock, Arnu Pretorius |
| 2024 | Dissect Black Box: Interpreting for Rule-Based Explanations in Unsupervised Anomaly Detection. Yu Zhang, Ruoyu Li, Nengwu Wu, Qing Li, Xinhan Lin, Yang Hu, Tao Li, Yong Jiang |
| 2024 | Dissecting Query-Key Interaction in Vision Transformers. Xu Pan, Aaron Philip, Ziqian Xie, Odelia Schwartz |
| 2024 | Dissecting the Failure of Invariant Learning on Graphs. Qixun Wang, Yifei Wang, Yisen Wang, Xianghua Ying |
| 2024 | Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers. Lorenzo Tiberi, Francesca Mignacco, Kazuki Irie, Haim Sompolinsky |
| 2024 | DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features. Letian Wang, Seung Wook Kim, Jiawei Yang, Cunjun Yu, Boris Ivanovic, Steven L. Waslander, Yue Wang, Sanja Fidler, Marco Pavone, Péter Karkus |
| 2024 | Distributed Least Squares in Small Space via Sketching and Bias Reduction. Sachin Garg, Kevin Tan, Michal Derezinski |
| 2024 | Distributed-Order Fractional Graph Operating Network. Kai Zhao, Xuhao Li, Qiyu Kang, Feng Ji, Qinxu Ding, Yanan Zhao, Wenfei Liang, Wee Peng Tay |
| 2024 | Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation. Zhiyi Pan, Wei Gao, Shan Liu, Ge Li |
| 2024 | Distribution Learning with Valid Outputs Beyond the Worst-Case. Nicholas Rittler, Kamalika Chaudhuri |
| 2024 | Distribution-Aware Data Expansion with Diffusion Models. Haowei Zhu, Ling Yang, Jun-Hai Yong, Hongzhi Yin, Jiawei Jiang, Meng Xiao, Wentao Zhang, Bin Wang |
| 2024 | Distributional Preference Alignment of LLMs via Optimal Transport. Igor Melnyk, Youssef Mroueh, Brian Belgodere, Mattia Rigotti, Apoorva Nitsure, Mikhail Yurochkin, Kristjan H. Greenewald, Jirí Navrátil, Jarret Ross |
| 2024 | Distributional Reinforcement Learning with Regularized Wasserstein Loss. Ke Sun, Yingnan Zhao, Wulong Liu, Bei Jiang, Linglong Kong |
| 2024 | Distributional Successor Features Enable Zero-Shot Policy Optimization. Chuning Zhu, Xinqi Wang, Tyler Han, Simon S. Du, Abhishek Gupta |
| 2024 | Distributional regression: CRPS-error bounds for model fitting, model selection and convex aggregation. Dombry Clement, Ahmed Zaoui |
| 2024 | Distributionally Robust Performative Prediction. Songkai Xue, Yuekai Sun |
| 2024 | Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms. Miao Lu, Han Zhong, Tong Zhang, Jose H. Blanchet |
| 2024 | DistrictNet: Decision-aware learning for geographical districting. Cheikh Ahmed, Alexandre Forel, Axel Parmentier, Thibaut Vidal |
| 2024 | Divergences between Language Models and Human Brains. Yuchen Zhou, Emmy Liu, Graham Neubig, Michael J. Tarr, Leila Wehbe |
| 2024 | Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec. Jun-Hyuk Kim, Seungeon Kim, Won-Hee Lee, Dokwan Oh |
| 2024 | Diversity Is Not All You Need: Training A Robust Cooperative Agent Needs Specialist Partners. Rujikorn Charakorn, Poramate Manoonpong, Nat Dilokthanakul |
| 2024 | Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment. Jiawei Du, Xin Zhang, Juncheng Hu, Wenxin Huang, Joey Tianyi Zhou |
| 2024 | Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation. Jingchang Chen, Hongxuan Tang, Zheng Chu, Qianglong Chen, Zekun Wang, Ming Liu, Bing Qin |
| 2024 | Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors. Yazid Janati, Badr Moufad, Alain Durmus, Eric Moulines, Jimmy Olsson |
| 2024 | Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm. Eli Sennesh, Hao Wu, Tommaso Salvatori |
| 2024 | Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? - A Theoretical and Empirical Study. Sangwon Jung, Sumin Yu, Sanghyuk Chun, Taesup Moon |
| 2024 | Do Finetti: On Causal Effects for Exchangeable Data. Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszar, Bernhard Schölkopf |
| 2024 | Do LLMs Build World Representations? Probing Through the Lens of State Abstraction. Zichao Li, Yanshuai Cao, Jackie CK Cheung |
| 2024 | Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam |
| 2024 | Do causal predictors generalize better to new domains? Vivian Y. Nastl, Moritz Hardt |
| 2024 | Do's and Don'ts: Learning Desirable Skills with Instruction Videos. Hyunseung Kim, Byungkun Lee, Hojoon Lee, Dongyoon Hwang, Donghu Kim, Jaegul Choo |
| 2024 | DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting. Binqian Xu, Xiangbo Shu, Haiyang Mei, Zechen Bai, Basura Fernando, Mike Zheng Shou, Jinhui Tang |
| 2024 | Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors. Jiashi Gao, Ziwei Wang, Xiangyu Zhao, Xin Yao, Xuetao Wei |
| 2024 | Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models. Javier González, Aditya V. Nori |
| 2024 | Does Video-Text Pretraining Help Open-Vocabulary Online Action Detection? Qingsong Zhao, Yi Wang, Jilan Xu, Yinan He, Zifan Song, Limin Wang, Yu Qiao, Cairong Zhao |
| 2024 | Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD. Jie Hu, Yi-Ting Ma, Do Young Eun |
| 2024 | Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning. Top Piriyakulkij, Cassidy Langenfeld, Tuan Anh Le, Kevin Ellis |
| 2024 | Domain Adaptation for Large-Vocabulary Object Detectors. Kai Jiang, Jiaxing Huang, Weiying Xie, Jie Lei, Yunsong Li, Ling Shao, Shijian Lu |
| 2024 | DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning. Yuxuan Duan, Yan Hong, Bo Zhang, Jun Lan, Huijia Zhu, Weiqiang Wang, Jianfu Zhang, Li Niu, Liqing Zhang |
| 2024 | Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences. Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik |
| 2024 | Don't Look Twice: Faster Video Transformers with Run-Length Tokenization. Rohan Choudhury, Guanglei Zhu, Sihan Liu, Koichiro Niinuma, Kris Kitani, László A. Jeni |
| 2024 | Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov |
| 2024 | Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search. Kevin Yu, Jihye Roh, Ziang Li, Wenhao Gao, Runzhong Wang, Connor W. Coley |
| 2024 | Doubly Hierarchical Geometric Representations for Strand-based Human Hairstyle Generation. Yunlu Chen, Francisco Vicente Carrasco, Christian Häne, Giljoo Nam, Jean-Charles Bazin, Fernando De la Torre |
| 2024 | Doubly Mild Generalization for Offline Reinforcement Learning. Yixiu Mao, Qi Wang, Yun Qu, Yuhang Jiang, Xiangyang Ji |
| 2024 | Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization. Ronak Mehta, Jelena Diakonikolas, Zaïd Harchaoui |
| 2024 | DreamCatcher: A Wearer-aware Multi-modal Sleep Event Dataset Based on Earables in Non-restrictive Environments. Zeyu Wang, Xiyuxing Zhang, Ruotong Yu, Yuntao Wang, Kenneth Christofferson, Jingru Zhang, Alex Mariakakis, Yuanchun Shi |
| 2024 | DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation. Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Xiaotian Han, Zhengyu Chen, Quanzeng You, Hongxia Yang |
| 2024 | DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation. Zhiqi Li, Yiming Chen, Peidong Liu |
| 2024 | DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos. Wen-Hsuan Chu, Lei Ke, Katerina Fragkiadaki |
| 2024 | DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models. Zhengyang Yu, Zhaoyuan Yang, Jing Zhang |
| 2024 | Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data. Kai Helli, David Schnurr, Noah Hollmann, Samuel Müller, Frank Hutter |
| 2024 | DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks. Mohamed Elrefaie, Florin Morar, Angela Dai, Faez Ahmed |
| 2024 | DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model. Yuqi Wang, Ke Cheng, Jiawei He, Qitai Wang, Hengchen Dai, Yuntao Chen, Fei Xia, Zhao-Xiang Zhang |
| 2024 | Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and Beyond. Zhechao Wang, Peirui Cheng, Minxing Chen, Pengju Tian, Zhirui Wang, Xinming Li, Xue Yang, Xian Sun |
| 2024 | DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation. Sunghyeon Woo, Baeseong Park, Byeongwook Kim, Minjung Jo, Se Jung Kwon, Dongsuk Jeon, Dongsoo Lee |
| 2024 | DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks. Zeyu Zhang, Lu Li, Shuyan Wan, Sijie Wang, Zhiyi Wang, Zhiyuan Lu, Dong Hao, Wanli Li |
| 2024 | Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals. Hui Zheng, Haiteng Wang, Wei-Bang Jiang, Zhongtao Chen, Li He, Pei-Yang Lin, Peng-Hu Wei, Guo-Guang Zhao, Yun-Zhe Liu |
| 2024 | DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs. Haokun Lin, Haobo Xu, Yichen Wu, Jingzhi Cui, Yingtao Zhang, Linzhan Mou, Linqi Song, Zhenan Sun, Ying Wei |
| 2024 | Dual Cone Gradient Descent for Training Physics-Informed Neural Networks. Youngsik Hwang, Dong-Young Lim |
| 2024 | Dual Critic Reinforcement Learning under Partial Observability. Jinqiu Li, Enmin Zhao, Tong Wei, Junliang Xing, Shiming Xiang |
| 2024 | Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning. Runhua Xu, Shiqi Gao, Chao Li, James Joshi, Jianxin Li |
| 2024 | Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images. Bahri Batuhan Bilecen, Ahmet Berke Gökmen, Aysegul Dundar |
| 2024 | Dual Lagrangian Learning for Conic Optimization. Mathieu Tanneau, Pascal Van Hentenryck |
| 2024 | Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models. Ce Zhang, Simon Stepputtis, Katia P. Sycara, Yaqi Xie |
| 2024 | Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models. Kaican Li, Weiyan Xie, Yongxiang Huang, Didan Deng, Lanqing Hong, Zhenguo Li, Ricardo Silva, Nevin L. Zhang |
| 2024 | Dual-Diffusion for Binocular 3D Human Pose Estimation. Xiaoyue Wan, Zhuo Chen, Bingzhi Duan, Xu Zhao |
| 2024 | Dual-Personalizing Adapter for Federated Foundation Models. Yiyuan Yang, Guodong Long, Tao Shen, Jing Jiang, Michael Blumenstein |
| 2024 | Dual-Perspective Activation: Efficient Channel Denoising via Joint Forward-Backward Criterion for Artificial Neural Networks. Tian Qiu, Chenchao Gao, Zunlei Feng, Jie Lei, Bingde Hu, Xingen Wang, Yi Gao, Mingli Song |
| 2024 | Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss. Yifei Zhang, Huan-ang Gao, Zhou Jiang, Hao Zhao |
| 2024 | Dueling over Dessert, Mastering the Art of Repeated Cake Cutting. Simina Brânzei, MohammadTaghi Hajiaghayi, Reed C. Phillips, Suho Shin, Kun Wang |
| 2024 | DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning. Anthony Liang, Guy Tennenholtz, Chih-Wei Hsu, Yinlam Chow, Erdem Biyik, Craig Boutilier |
| 2024 | DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control. Zichen Jeff Cui, Hengkai Pan, Aadhithya Iyer, Siddhant Haldar, Lerrel Pinto |
| 2024 | Dynamic 3D Gaussian Fields for Urban Areas. Tobias Fischer, Jonas Kulhanek, Samuel Rota Bulò, Lorenzo Porzi, Marc Pollefeys, Peter Kontschieder |
| 2024 | Dynamic Conditional Optimal Transport through Simulation-Free Flows. Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth |
| 2024 | Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning. Arko Banerjee, Kia Rahmani, Joydeep Biswas, Isil Dillig |
| 2024 | Dynamic Neural Regeneration: Enhancing Deep Learning Generalization on Small Datasets. Vijaya Raghavan T. Ramkumar, Elahe Arani, Bahram Zonooz |
| 2024 | Dynamic Rescaling for Training GNNs. Nimrah Mustafa, Rebekka Burkholz |
| 2024 | Dynamic Service Fee Pricing under Strategic Behavior: Actions as Instruments and Phase Transition. Rui Ai, David Simchi-Levi, Feng Zhu |
| 2024 | Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments. Yanping Li, Jingshen Wang, Waverly Wei |
| 2024 | Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation. Wangbo Zhao, Jiasheng Tang, Yizeng Han, Yibing Song, Kai Wang, Gao Huang, Fan Wang, Yang You |
| 2024 | Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron. Christian Schmid, James M. Murray |
| 2024 | Déjà Vu Memorization in Vision-Language Models. Bargav Jayaraman, Chuan Guo, Kamalika Chaudhuri |
| 2024 | E Wang Lin, Yueying Feng, WenKang Han, Tao Jin, Zhou Zhao, Fei Wu, Chang Yao, Jingyuan Chen |
| 2024 | E-Motion: Future Motion Simulation via Event Sequence Diffusion. Song Wu, Zhiyu Zhu, Junhui Hou, Guangming Shi, Jinjian Wu |
| 2024 | E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding. Ye Liu, Zongyang Ma, Zhongang Qi, Yang Wu, Ying Shan, Chang Wen Chen |
| 2024 | E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection. Jiaqing Zhang, Mingxiang Cao, Weiying Xie, Jie Lei, Daixun Li, Wenbo Huang, Yunsong Li, Xue Yang |
| 2024 | E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu |
| 2024 | EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding. Thanh-Dat Truong, Utsav Prabhu, Dongyi Wang, Bhiksha Raj, Susan Gauch, Jeyamkondan Subbiah, Khoa Luu |
| 2024 | EAI: Emotional Decision-Making of LLMs in Strategic Games and Ethical Dilemmas. Mikhail Mozikov, Nikita Severin, Valeria Bodishtianu, Maria Glushanina, Ivan Nasonov, Daniil Orekhov, Pekhotin Vladislav, Ivan Makovetskiy, Mikhail Baklashkin, Vasily Lavrentyev, Akim Tsvigun, Denis Turdakov, Tatiana Shavrina, Andrey V. Savchenko, Ilya Makarov |
| 2024 | EASI: Evolutionary Adversarial Simulator Identification for Sim-to-Real Transfer. Haoyu Dong, Huiqiao Fu, Wentao Xu, Zhehao Zhou, Chunlin Chen |
| 2024 | ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks. Yuezhu Xu, S. Sivaranjani |
| 2024 | ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction. Wei Dong, Han Zhou, Yulun Zhang, Xiaohong Liu, Jun Chen |
| 2024 | EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching. Xinwang Chen, Ning Liu, Yichen Zhu, Feifei Feng, Jian Tang |
| 2024 | EEG2Video: Towards Decoding Dynamic Visual Perception from EEG Signals. Xuan-Hao Liu, Yan-Kai Liu, Yansen Wang, Kan Ren, Hanwen Shi, Zilong Wang, Dongsheng Li, Bao-Liang Lu, Wei-Long Zheng |
| 2024 | EEGPT: Pretrained Transformer for Universal and Reliable Representation of EEG Signals. Guangyu Wang, Wenchao Liu, Yuhong He, Cong Xu, Lin Ma, Haifeng Li |
| 2024 | EEVR: A Dataset of Paired Physiological Signals and Textual Descriptions for Joint Emotion Representation Learning. Pragya Singh, Ritvik Budhiraja, Ankush Gupta, Anshul Goswami, Mohan Kumar, Pushpendra Singh |
| 2024 | EGODE: An Event-attended Graph ODE Framework for Modeling Rigid Dynamics. Jingyang Yuan, Gongbo Sun, Zhiping Xiao, Hang Zhou, Xiao Luo, Junyu Luo, Yusheng Zhao, Wei Ju, Ming Zhang |
| 2024 | EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection. Sheng Wu, Hang Sheng, Hui Feng, Bo Hu |
| 2024 | EGonc : Energy-based Open-Set Node Classification with substitute Unknowns. Qin Zhang, Zelin Shi, Shirui Pan, Junyang Chen, Huisi Wu, Xiaojun Chen |
| 2024 | EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records. Yeonsu Kwon, Jiho Kim, Gyubok Lee, Seongsu Bae, Daeun Kyung, Wonchul Cha, Tom J. Pollard, Alistair Johnson, Edward Choi |
| 2024 | EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries. Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak, Dongchul Cha, Hangyul Yoon, Kwang Kim, Jeewon Yang, Seunghyun Won, Edward Choi |
| 2024 | EM Distillation for One-step Diffusion Models. Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin P. Murphy, Tim Salimans, Ben Poole, Ruiqi Gao |
| 2024 | EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography. Jehan Yang, Maxwell Soh, Vivianna Lieu, Douglas J. Weber, Zackory Erickson |
| 2024 | EMR-Merging: Tuning-Free High-Performance Model Merging. Chenyu Huang, Peng Ye, Tao Chen, Tong He, Xiangyu Yue, Wanli Ouyang |
| 2024 | EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing. Qibo Qiu, Shun Zhang, Haiming Gao, Honghui Yang, Haochao Ying, Wenxiao Wang, Xiaofei He |
| 2024 | ENAT: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis. Zanlin Ni, Yulin Wang, Renping Zhou, Yizeng Han, Jiayi Guo, Zhiyuan Liu, Yuan Yao, Gao Huang |
| 2024 | EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models. Jinhee Kim, Taesung Kim, Jaegul Choo |
| 2024 | ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models. Jio Oh, Soyeon Kim, Junseok Seo, Jindong Wang, Ruochen Xu, Xing Xie, Steven Whang |
| 2024 | ESPACE: Dimensionality Reduction of Activations for Model Compression. Charbel Sakr, Brucek Khailany |
| 2024 | ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation. Majdi Hassan, Nikhil Shenoy, Jungyoon Lee, Hannes Stärk, Stephan Thaler, Dominique Beaini |
| 2024 | ETO: Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses. Junjie Ni, Guofeng Zhang, Guanglin Li, Yijin Li, Xinyang Liu, Zhaoyang Huang, Hujun Bao |
| 2024 | EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection. Qinqian Lei, Bo Wang, Robby T. Tan |
| 2024 | Easy Regional Contrastive Learning of Expressive Fashion Representations. Daiqing Qi, Handong Zhao, Sheng Li |
| 2024 | Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision. Zhiqing Sun, Longhui Yu, Yikang Shen, Weiyang Liu, Yiming Yang, Sean Welleck, Chuang Gan |
| 2024 | Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Animashree Anandkumar, Furong Huang |
| 2024 | Edit Distance Robust Watermarks via Indexing Pseudorandom Codes. Noah Golowich, Ankur Moitra |
| 2024 | Effective Exploration Based on the Structural Information Principles. Xianghua Zeng, Hao Peng, Angsheng Li |
| 2024 | Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting. Junha Hyung, Susung Hong, Sungwon Hwang, Jaeseong Lee, Jaegul Choo, Jin-Hwa Kim |
| 2024 | EffiBench: Benchmarking the Efficiency of Automatically Generated Code. Dong Huang, Yuhao Qing, Weiyi Shang, Heming Cui, Jie Zhang |
| 2024 | EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization. Dong Huang, Jianbo Dai, Han Weng, Puzhen Wu, Yuhao Qing, Heming Cui, Zhijiang Guo, Jie Zhang |
| 2024 | Efficiency for Free: Ideal Data Are Transportable Representations. Peng Sun, Yi Jiang, Tao Lin |
| 2024 | Efficiency of the First-Price Auction in the Autobidding World. Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo |
| 2024 | Efficient $\Phi$-Regret Minimization with Low-Degree Swap Deviations in Extensive-Form Games. Brian Hu Zhang, Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm |
| 2024 | Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation. Wei Dong, Yuan Sun, Yiting Yang, Xing Zhang, Zhijun Lin, Qingsen Yan, Haokui Zhang, Peng Wang, Yang Yang, Hengtao Shen |
| 2024 | Efficient Adversarial Training in LLMs with Continuous Attacks. Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn |
| 2024 | Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously. Yihan Wang, Yifan Zhu, Xiao-Shan Gao |
| 2024 | Efficient Centroid-Linkage Clustering. Mohammad Hossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N. Gowda, D. Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki |
| 2024 | Efficient Combinatorial Optimization via Heat Diffusion. Hengyuan Ma, Wenlian Lu, Jianfeng Feng |
| 2024 | Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning. Xuechen Zhang, Zijian Huang, Ege Onur Taga, Carlee Joe-Wong, Samet Oymak, Jiasi Chen |
| 2024 | Efficient Discrepancy Testing for Learning with Distribution Shift. Gautam Chandrasekaran, Adam R. Klivans, Vasilis Kontonis, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2024 | Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability. Ming Xiang, Stratis Ioannidis, Edmund Yeh, Carlee Joe-Wong, Lili Su |
| 2024 | Efficient Graph Matching for Correlated Stochastic Block Models. Shuwen Chai, Miklós Z. Rácz |
| 2024 | Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained Optimization. Kai Hu, Weichen Yu, Yining Li, Tianjun Yao, Xiang Li, Wenhe Liu, Lijun Yu, Zhiqiang Shen, Kai Chen, Matt Fredrikson |
| 2024 | Efficient LLM Scheduling by Learning to Rank. Yichao Fu, Siqi Zhu, Runlong Su, Aurick Qiao, Ion Stoica, Hao Zhang |
| 2024 | Efficient Large Multi-modal Models via Visual Context Compression. Jieneng Chen, Luoxin Ye, Ju He, Zhaoyang Wang, Daniel Khashabi, Alan L. Yuille |
| 2024 | Efficient Leverage Score Sampling for Tensor Train Decomposition. Vivek Bharadwaj, Beheshteh T. Rakhshan, Osman Asif Malik, Guillaume Rabusseau |
| 2024 | Efficient Lifelong Model Evaluation in an Era of Rapid Progress. Ameya Prabhu, Vishaal Udandarao, Philip Torr, Matthias Bethge, Adel Bibi, Samuel Albanie |
| 2024 | Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms. Firas Trabelsi, David Vilar, Mara Finkelstein, Markus Freitag |
| 2024 | Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters. Yifei Xia, Fangcheng Fu, Wentao Zhang, Jiawei Jiang, Bin Cui |
| 2024 | Efficient Multi-task Reinforcement Learning with Cross-Task Policy Guidance. Jinmin He, Kai Li, Yifan Zang, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng |
| 2024 | Efficient Policy Evaluation Across Multiple Different Experimental Datasets. Yonghan Jung, Alexis Bellot |
| 2024 | Efficient Prompt Optimization Through the Lens of Best Arm Identification. Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li, Jing Yang, Cong Shen |
| 2024 | Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate. Fan-Ming Luo, Zuolin Tu, Zefang Huang, Yang Yu |
| 2024 | Efficient Reinforcement Learning by Discovering Neural Pathways. Samin Yeasar Arnob, Riyasat Ohib, Sergey M. Plis, Amy Zhang, Alessandro Sordoni, Doina Precup |
| 2024 | Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction. Wei Jiang, Sifan Yang, Wenhao Yang, Lijun Zhang |
| 2024 | Efficient Sketches for Training Data Attribution and Studying the Loss Landscape. Andrea Schioppa |
| 2024 | Efficient Streaming Algorithms for Graphlet Sampling. Yann Bourreau, Marco Bressan, T.-H. Hubert Chan, Qipeng Kuang, Mauro Sozio |
| 2024 | Efficient Temporal Action Segmentation via Boundary-aware Query Voting. Peiyao Wang, Yuewei Lin, Erik Blasch, Jie Wei, Haibin Ling |
| 2024 | Efficient and Private Marginal Reconstruction with Local Non-Negativity. Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron Musco, Daniel R. Sheldon |
| 2024 | Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang |
| 2024 | Efficient multi-prompt evaluation of LLMs. Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin |
| 2024 | EfficientCAPER: An End-to-End Framework for Fast and Robust Category-Level Articulated Object Pose Estimation. Xinyi Yu, Haonan Jiang, Li Zhang, Lin Yuanbo Wu, Linlin Ou, Liu Liu |
| 2024 | Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing. Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, Shuigeng Zhou |
| 2024 | EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views. Yuhang Yang, Wei Zhai, Chengfeng Wang, Chengjun Yu, Yang Cao, Zheng-Jun Zha |
| 2024 | EgoSim: An Egocentric Multi-view Simulator and Real Dataset for Body-worn Cameras during Motion and Activity. Dominik Hollidt, Paul Streli, Jiaxi Jiang, Yasaman Haghighi, Changlin Qian, Xintong Liu, Christian Holz |
| 2024 | EigenVI: score-based variational inference with orthogonal function expansions. Diana Cai, Chirag Modi, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul |
| 2024 | Einsum Benchmark: Enabling the Development of Next-Generation Tensor Execution Engines. Mark Blacher, Christoph Staudt, Julien Klaus, Maurice Wenig, Niklas Merk, Alexander Breuer, Max Engel, Sören Laue, Joachim Giesen |
| 2024 | ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer. Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, Jia Li |
| 2024 | Elliptical Attention. Stefan K. Nielsen, Laziz U. Abdullaev, Rachel S. Y. Teo, Tan Nguyen |
| 2024 | Elo Uncovered: Robustness and Best Practices in Language Model Evaluation. Meriem Boubdir, Edward Kim, Beyza Ermis, Sara Hooker, Marzieh Fadaee |
| 2024 | Elucidating the Design Space of Dataset Condensation. Shitong Shao, Zikai Zhou, Huanran Chen, Zhiqiang Shen |
| 2024 | Embedding Dimension of Contrastive Learning and k-Nearest Neighbors. Dmitrii Avdiukhin, Vaggos Chatziafratis, Orr Fischer, Grigory Yaroslavtsev |
| 2024 | Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning. Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong, Zhuosheng Zhang, Rui Wang |
| 2024 | Embedding-Aligned Language Models. Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Lior Shani, Yi Liang, Craig Boutilier |
| 2024 | Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making. Manling Li, Shiyu Zhao, Qineng Wang, Kangrui Wang, Yu Zhou, Sanjana Srivastava, Cem Gokmen, Tony Lee, Li Erran Li, Ruohan Zhang, Weiyu Liu, Percy Liang, Li Fei-Fei, Jiayuan Mao, Jiajun Wu |
| 2024 | Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space. Core Francisco Park, Maya Okawa, Andrew Lee, Ekdeep Singh Lubana, Hidenori Tanaka |
| 2024 | Emergence of heavy tails in homogenized stochastic gradient descent. Zhezhe Jiao, Martin Keller-Ressel |
| 2024 | Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning. Zebang Cheng, Zhi-Qi Cheng, Jun-Yan He, Kai Wang, Yuxiang Lin, Zheng Lian, Xiaojiang Peng, Alexander G. Hauptmann |
| 2024 | Empowering Active Learning for 3D Molecular Graphs with Geometric Graph Isomorphism. Ronast Subedi, Lu Wei, Wenhan Gao, Shayok Chakraborty, Yi Liu |
| 2024 | Empowering Visible-Infrared Person Re-Identification with Large Foundation Models. Zhangyi Hu, Bin Yang, Mang Ye |
| 2024 | Empowering and Assessing the Utility of Large Language Models in Crop Science. Hang Zhang, Jiawei Sun, Renqi Chen, Wei Liu, Zhonghang Yuan, Xinzhe Zheng, Zhefan Wang, Zhiyuan Yang, Hang Yan, Han-Sen Zhong, Xiqing Wang, Wanli Ouyang, Fan Yang, Nanqing Dong |
| 2024 | EnOF-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature. Yufei Guo, Weihang Peng, Xiaode Liu, Yuanpei Chen, Yuhan Zhang, Xin Tong, Zhou Jie, Zhe Ma |
| 2024 | Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity. Robby Costales, Stefanos Nikolaidis |
| 2024 | End-To-End Causal Effect Estimation from Unstructured Natural Language Data. Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison |
| 2024 | End-to-End Ontology Learning with Large Language Models. Andy Lo, Albert Q. Jiang, Wenda Li, Mateja Jamnik |
| 2024 | End-to-End Video Semantic Segmentation in Adverse Weather using Fusion Blocks and Temporal-Spatial Teacher-Student Learning. Xin Yang, Wending Yan, Michael Bi Mi, Yuan Yuan, Robby T. Tan |
| 2024 | End-to-end Learnable Clustering for Intent Learning in Recommendation. Yue Liu, Shihao Zhu, Jun Xia, Yingwei Ma, Jian Ma, Xinwang Liu, Shengju Yu, Kejun Zhang, Wenliang Zhong |
| 2024 | Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces. Tobias Schröder, Zijing Ou, Yingzhen Li, Andrew B. Duncan |
| 2024 | Energy-Guided Continuous Entropic Barycenter Estimation for General Costs. Alexander Kolesov, Petr Mokrov, Igor Udovichenko, Milena Gazdieva, Gudmund Pammer, Anastasis Kratsios, Evgeny Burnaev, Aleksandr Korotin |
| 2024 | Energy-based Epistemic Uncertainty for Graph Neural Networks. Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann |
| 2024 | Energy-based Hopfield Boosting for Out-of-Distribution Detection. Claus Hofmann, Simon Schmid, Bernhard Lehner, Daniel Klotz, Sepp Hochreiter |
| 2024 | Enhancing Chess Reinforcement Learning with Graph Representation. Tomas Rigaux, Hisashi Kashima |
| 2024 | Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination. Shelly Golan, Roy Ganz, Michael Elad |
| 2024 | Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA. David Smerkous, Qinxun Bai, Fuxin Li |
| 2024 | Enhancing Domain Adaptation through Prompt Gradient Alignment. Viet Hoang Phan, Tung Lam Tran, Quyen Tran, Trung Le |
| 2024 | Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation. Shangding Gu, Laixi Shi, Yuhao Ding, Alois Knoll, Costas J. Spanos, Adam Wierman, Ming Jin |
| 2024 | Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers. Chau Pham, Bryan A. Plummer |
| 2024 | Enhancing Graph Transformers with Hierarchical Distance Structural Encoding. Yuankai Luo, Hongkang Li, Lei Shi, Xiao-Ming Wu |
| 2024 | Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective. Xinhao Yao, Xiaolin Hu, Shenzhi Yang, Yong Liu |
| 2024 | Enhancing LLM Reasoning via Vision-Augmented Prompting. Ziyang Xiao, Dongxiang Zhang, Xiongwei Han, Xiaojin Fu, Wing Yin Yu, Tao Zhong, Sai Wu, Yuan Wang, Jianwei Yin, Gang Chen |
| 2024 | Enhancing LLM's Cognition via Structurization. Kai Liu, Zhihang Fu, Chao Chen, Wei Zhang, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, Jieping Ye |
| 2024 | Enhancing Large Language Models through Adaptive Tokenizers. Mengyu Zheng, Hanting Chen, Tianyu Guo, Chong Zhu, Binfan Zheng, Chang Xu, Yunhe Wang |
| 2024 | Enhancing Large Vision Language Models with Self-Training on Image Comprehension. Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, Quanquan Gu, James Y. Zou, Kai-Wei Chang, Wei Wang |
| 2024 | Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning. Penghui Ruan, Pichao Wang, Divya Saxena, Jiannong Cao, Yuhui Shi |
| 2024 | Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control. Yuxin Xiao, Chaoqun Wan, Yonggang Zhang, Wenxiao Wang, Binbin Lin, Xiaofei He, Xu Shen, Jieping Ye |
| 2024 | Enhancing Preference-based Linear Bandits via Human Response Time. Shen Li, Yuyang Zhang, Zhaolin Ren, Claire Liang, Na Li, Julie A. Shah |
| 2024 | Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework. Ruihan Guo, Rui Wang, Ruidong Wu, Zhizhou Ren, Jiahan Li, Shitong Luo, Zuofan Wu, Qiang Liu, Jian Peng, Jianzhu Ma |
| 2024 | Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus. Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa |
| 2024 | Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach. Rory Young, Nicolas Pugeault |
| 2024 | Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning. Yuefei Lyu, Chaozhuo Li, Sihong Xie, Xi Zhang |
| 2024 | Enhancing Robustness of Last Layer Two-Stage Fair Model Corrections. Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo, Richard Nock, Lalitha Sankar |
| 2024 | Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection. Qian Shao, Jiangrui Kang, Qiyuan Chen, Zepeng Li, Hongxia Xu, Yiwen Cao, Jiajuan Liang, Jian Wu |
| 2024 | Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting. Xingyu Zhu, Beier Zhu, Yi Tan, Shuo Wang, Yanbin Hao, Hanwang Zhang |
| 2024 | Enhancing vision-language models for medical imaging: bridging the 3D gap with innovative slice selection. Yuli Wang, Peng Jian, Yuwei Dai, Craig K. Jones, Haris I. Sair, Jinglai Shen, Nicolas Loizou, Jing Wu, Wen-Chi Hsu, Maliha Imami, Zhicheng Jiao, Paul Zhang, Harrison X. Bai |
| 2024 | Enriching Disentanglement: From Logical Definitions to Quantitative Metrics. Yivan Zhang, Masashi Sugiyama |
| 2024 | EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models. Shangquan Sun, Wenqi Ren, Zikun Liu, Hyunhee Park, Rui Wang, Xiaochun Cao |
| 2024 | Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration. Yichong Huang, Xiaocheng Feng, Baohang Li, Yang Xiang, Hui Wang, Ting Liu, Bing Qin |
| 2024 | Ensemble sampling for linear bandits: small ensembles suffice. David Janz, Alexander E. Litvak, Csaba Szepesvári |
| 2024 | Entity Alignment with Noisy Annotations from Large Language Models. Shengyuan Chen, Qinggang Zhang, Junnan Dong, Wen Hua, Qing Li, Xiao Huang |
| 2024 | Entropy testing and its application to testing Bayesian networks. Clément L. Canonne, Joy Qiping Yang |
| 2024 | Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning. Ruoqi Zhang, Ziwei Luo, Jens Sjölund, Thomas B. Schön, Per Mattsson |
| 2024 | Entrywise error bounds for low-rank approximations of kernel matrices. Alexander Modell |
| 2024 | EpiCare: A Reinforcement Learning Benchmark for Dynamic Treatment Regimes. Mason Hargrave, Alex Spaeth, Logan Grosenick |
| 2024 | Epipolar-Free 3D Gaussian Splatting for Generalizable Novel View Synthesis. Zhiyuan Min, Yawei Luo, Jianwen Sun, Yi Yang |
| 2024 | Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning. Dongsu Lee, Minhae Kwon |
| 2024 | Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation. Jiwoong Park, Yang Shen |
| 2024 | Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters. Ya-Wei Eileen Lin, Ronen Talmon, Ron Levie |
| 2024 | Equivariant Neural Diffusion for Molecule Generation. François R. J. Cornet, Grigory Bartosh, Mikkel N. Schmidt, Christian Andersson Naesseth |
| 2024 | Equivariant spatio-hemispherical networks for diffusion MRI deconvolution. Axel Elaldi, Guido Gerig, Neel Dey |
| 2024 | Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention. Peng Li, Yuan Liu, Xiaoxiao Long, Feihu Zhang, Cheng Lin, Mengfei Li, Xingqun Qi, Shanghang Zhang, Wei Xue, Wenhan Luo, Ping Tan, Wenping Wang, Qifeng Liu, Yike Guo |
| 2024 | Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation. Anh Bui, Tung-Long Vuong, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham, Dinh Q. Phung |
| 2024 | Error Analysis of Spherically Constrained Least Squares Reformulation in Solving the Stackelberg Prediction Game. Xiyuan Li, Weiwei Liu |
| 2024 | Error Correction Output Codes for Robust Neural Networks against Weight-errors: A Neural Tangent Kernel Point of View. Anlan Yu, Shusen Jing, Ning Lyu, Wujie Wen, Zhiyuan Yan |
| 2024 | Estimating Ego-Body Pose from Doubly Sparse Egocentric Video Data. Seunggeun Chi, Pin-Hao Huang, Enna Sachdeva, Hengbo Ma, Karthik Ramani, Kwonjoon Lee |
| 2024 | Estimating Epistemic and Aleatoric Uncertainty with a Single Model. Matthew Chan, Maria Molina, Chris Metzler |
| 2024 | Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression. Kai Tan, Pierre C. Bellec |
| 2024 | Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data. Miruna Oprescu, Nathan Kallus |
| 2024 | Estimating the Hallucination Rate of Generative AI. Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei |
| 2024 | Euclidean distance compression via deep random features. Brett Leroux, Luis Rademacher |
| 2024 | Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering. Fangdi Wang, Jiaqi Jin, Jingtao Hu, Suyuan Liu, Xihong Yang, Siwei Wang, Xinwang Liu, En Zhu |
| 2024 | Evaluating Copyright Takedown Methods for Language Models. Boyi Wei, Weijia Shi, Yangsibo Huang, Noah A. Smith, Chiyuan Zhang, Luke Zettlemoyer, Kai Li, Peter Henderson |
| 2024 | Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads. Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit, Joanna Matthiesen, Kevin A. Smith, Josh Tenenbaum |
| 2024 | Evaluating Multiview Object Consistency in Humans and Image Models. Tyler Bonnen, Stephanie Fu, Yutong Bai, Thomas P. O'Connell, Yoni Friedman, Nancy Kanwisher, Josh Tenenbaum, Alexei A. Efros |
| 2024 | Evaluating Numerical Reasoning in Text-to-Image Models. Ivana Kajic, Olivia Wiles, Isabela Albuquerque, Matthias Bauer, Su Wang, Jordi Pont-Tuset, Aida Nematzadeh |
| 2024 | Evaluating alignment between humans and neural network representations in image-based learning tasks. Can Demircan, Tankred Saanum, Leonardo Pettini, Marcel Binz, Blazej M. Baczkowski, Christian F. Doeller, Mona M. Garvert, Eric Schulz |
| 2024 | Evaluating language models as risk scores. André F. Cruz, Moritz Hardt, Celestine Mendler-Dünner |
| 2024 | Evaluating the World Model Implicit in a Generative Model. Keyon Vafa, Justin Y. Chen, Ashesh Rambachan, Jon M. Kleinberg, Sendhil Mullainathan |
| 2024 | Evaluating the design space of diffusion-based generative models. Yuqing Wang, Ye He, Molei Tao |
| 2024 | Evaluation of Text-to-Video Generation Models: A Dynamics Perspective. Mingxiang Liao, Hannan Lu, Qixiang Ye, Wangmeng Zuo, Fang Wan, Tianyu Wang, Yuzhong Zhao, Jingdong Wang, Xinyu Zhang |
| 2024 | Even Sparser Graph Transformers. Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, David P. Woodruff, Danica J. Sutherland |
| 2024 | Event-3DGS: Event-based 3D Reconstruction Using 3D Gaussian Splatting. Haiqian Han, Jianing Li, Henglu Wei, Xiangyang Ji |
| 2024 | Everyday Object Meets Vision-and-Language Navigation Agent via Backdoor. Keji He, Kehan Chen, Jiawang Bai, Yan Huang, Qi Wu, Shu-Tao Xia, Liang Wang |
| 2024 | Evidence of Learned Look-Ahead in a Chess-Playing Neural Network. Erik Jenner, Shreyas Kapur, Vasil Georgiev, Cameron Allen, Scott Emmons, Stuart J. Russell |
| 2024 | Evidential Mixture Machines: Deciphering Multi-Label Correlations for Active Learning Sensitivity. Dayou Yu, Minghao Li, Weishi Shi, Qi Yu |
| 2024 | Evidential Stochastic Differential Equations for Time-Aware Sequential Recommendation. Krishna Prasad Neupane, Ervine Zheng, Qi Yu |
| 2024 | EvoCodeBench: An Evolving Code Generation Benchmark with Domain-Specific Evaluations. Jia Li, Ge Li, Xuanming Zhang, Yunfei Zhao, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li |
| 2024 | EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models. Rui Zhao, Hangjie Yuan, Yujie Wei, Shiwei Zhang, Yuchao Gu, Lingmin Ran, Xiang Wang, Jay Zhangjie Wu, David Junhao Zhang, Yingya Zhang, Mike Zheng Shou |
| 2024 | Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems. Giung Nam, Juho Lee |
| 2024 | Exact Gradients for Stochastic Spiking Neural Networks Driven by Rough Signals. Christian Holberg, Cristopher Salvi |
| 2024 | Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization. Davide Buffelli, Jamie McGowan, Wangkun Xu, Alexandru Cioba, Da-Shan Shiu, Guillaume Hennequin, Alberto Bernacchia |
| 2024 | Exactly Minimax-Optimal Locally Differentially Private Sampling. Hyun-Young Park, Shahab Asoodeh, Si-Hyeon Lee |
| 2024 | Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking. Roland Stolz, Hanna Krasowski, Jakob Thumm, Michael Eichelbeck, Philipp Gassert, Matthias Althoff |
| 2024 | Exclusively Penalized Q-learning for Offline Reinforcement Learning. Junghyuk Yeom, Yonghyeon Jo, Jeongmo Kim, Sanghyeon Lee, Seungyul Han |
| 2024 | Exocentric-to-Egocentric Video Generation. Jia-Wei Liu, Weijia Mao, Zhongcong Xu, Jussi Keppo, Mike Zheng Shou |
| 2024 | Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation. Yikang Chen, Dehui Du, Lili Tian |
| 2024 | Expanding Sparse Tuning for Low Memory Usage. Shufan Shen, Junshu Sun, Xiangyang Ji, Qingming Huang, Shuhui Wang |
| 2024 | Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch. Malek Mechergui, Sarath Sreedharan |
| 2024 | Expected Probabilistic Hierarchies. Marcel Kollovieh, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann |
| 2024 | Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport. Nazar Buzun, Maksim Bobrin, Dmitry V. Dylov |
| 2024 | Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection. Charles Guille-Escuret, Pierre-André Noël, Ioannis Mitliagkas, David Vázquez, João Monteiro |
| 2024 | Expert-level protocol translation for self-driving labs. Yu-Zhe Shi, Fanxu Meng, Haofei Hou, Zhangqian Bi, Qiao Xu, Lecheng Ruan, Qining Wang |
| 2024 | Explaining Datasets in Words: Statistical Models with Natural Language Parameters. Ruiqi Zhong, Heng Wang, Dan Klein, Jacob Steinhardt |
| 2024 | Explanations that reveal all through the definition of encoding. Aahlad Manas Puli, Nhi Nguyen, Rajesh Ranganath |
| 2024 | Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization. Haocheng Luo, Tuan Truong, Tung Pham, Mehrtash Harandi, Dinh Q. Phung, Trung Le |
| 2024 | Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering. Dongxiao He, Lianze Shan, Jitao Zhao, Hengrui Zhang, Zhen Wang, Weixiong Zhang |
| 2024 | Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion. Filip Szatkowski, Bartosz Wójcik, Mikolaj Piórczynski, Simone Scardapane |
| 2024 | Exploiting Descriptive Completeness Prior for Cross Modal Hashing with Incomplete Labels. Haoyang Luo, Zheng Zhang, Yadan Luo |
| 2024 | Exploiting LLM Quantization. Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin T. Vechev |
| 2024 | Exploiting Representation Curvature for Boundary Detection in Time Series. Yooju Shin, Jaehyun Park, Susik Yoon, Hwanjun Song, Byung Suk Lee, Jae-Gil Lee |
| 2024 | Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration. Hongming Zhang, Chenjun Xiao, Chao Gao, Han Wang, Bo Xu, Martin Müller |
| 2024 | Exploration by Learning Diverse Skills through Successor State Representations. Paul-Antoine Le Tolguenec, Yann Besse, Florent Teichteil-Königsbuch, Dennis Wilson, Emmanuel Rachelson |
| 2024 | Exploratory Retrieval-Augmented Planning For Continual Embodied Instruction Following. Minjong Yoo, Jinwoo Jang, Wei-Jin Park, Honguk Woo |
| 2024 | Exploring Adversarial Robustness of Deep State Space Models. Biqing Qi, Yiang Luo, Junqi Gao, Pengfei Li, Kai Tian, Zhiyuan Ma, Bowen Zhou |
| 2024 | Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models. Yule Wang, Chengrui Li, Weihan Li, Anqi Wu |
| 2024 | Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks. Xuyuan Liu, Yinghao Cai, Qihui Yang, Yujun Yan |
| 2024 | Exploring Context Window of Large Language Models via Decomposed Positional Vectors. Zican Dong, Junyi Li, Xin Men, Xin Zhao, Bingning Wang, Zhen Tian, Weipeng Chen, Ji-Rong Wen |
| 2024 | Exploring DCN-like architecture for fast image generation with arbitrary resolution. Shuai Wang, Zexian Li, Tianhui Song, Xubin Li, Tiezheng Ge, Bo Zheng, Limin Wang |
| 2024 | Exploring Fixed Point in Image Editing: Theoretical Support and Convergence Optimization. Chen Hang, Zhe Ma, Haoming Chen, Xuwei Fang, Vincent Xie, Faming Fang, Guixu Zhang, Hongbin Wang |
| 2024 | Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations. Artem Agafonov, Petr Ostroukhov, Roman Mozhaev, Konstantin Yakovlev, Eduard Gorbunov, Martin Takác, Alexander V. Gasnikov, Dmitry Kamzolov |
| 2024 | Exploring Low-Dimensional Subspace in Diffusion Models for Controllable Image Editing. Siyi Chen, Huijie Zhang, Minzhe Guo, Yifu Lu, Peng Wang, Qing Qu |
| 2024 | Exploring Molecular Pretraining Model at Scale. Xiaohong Ji, Zhen Wang, Zhifeng Gao, Hang Zheng, Linfeng Zhang, Guolin Ke, Weinan E |
| 2024 | Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation. Mingjia Li, Shuang Li, Tongrui Su, Longhui Yuan, Jian Liang, Wei Li |
| 2024 | Exploring Token Pruning in Vision State Space Models. Zheng Zhan, Zhenglun Kong, Yifan Gong, Yushu Wu, Zichong Meng, Hangyu Zheng, Xuan Shen, Stratis Ioannidis, Wei Niu, Pu Zhao, Yanzhi Wang |
| 2024 | Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks. Xin-Chun Li, Jin-Lin Tang, Bo Zhang, Lan Li, De-Chuan Zhan |
| 2024 | Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning. Yuanlin Duan, Guofeng Cui, He Zhu |
| 2024 | Exploring the Precise Dynamics of Single-Layer GAN Models: Leveraging Multi-Feature Discriminators for High-Dimensional Subspace Learning. Andrew Bond, Zafer Dogan |
| 2024 | Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models. Bingqi Ma, Zhuofan Zong, Guanglu Song, Hongsheng Li, Yu Liu |
| 2024 | Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces. Luis Cubillos, Guy Revach, Matthew Mender, Joseph T. Costello, Hisham Temmar, Aren Hite, Diksha Anoop Kumar Zutshi, Dylan Wallace, Xiaoyong Ni, Madison Kelberman, Matt S. Willsey, Ruud van Sloun, Nir Shlezinger, Parag G. Patil, Anne Draelos, Cynthia A. Chestek |
| 2024 | Exponential Quantum Communication Advantage in Distributed Inference and Learning. Dar Gilboa, Hagay Michaeli, Daniel Soudry, Jarrod R. McClean |
| 2024 | Expressive Gaussian Human Avatars from Monocular RGB Video. Hezhen Hu, Zhiwen Fan, Tianhao Wu, Yihan Xi, Seoyoung Lee, Georgios Pavlakos, Zhangyang Wang |
| 2024 | Extending Multi-modal Contrastive Representations. Ziang Zhang, Zehan Wang, Luping Liu, Rongjie Huang, Xize Cheng, Zhenhui Ye, Wang Lin, Huadai Liu, Haifeng Huang, Yang Zhao, Tao Jin, Siqi Zheng, Zhou Zhao |
| 2024 | Extending Video Masked Autoencoders to 128 frames. Nitesh Bharadwaj Gundavarapu, Luke Friedman, Raghav Goyal, Chaitra Hegde, Eirikur Agustsson, Sagar Waghmare, Mikhail Sirotenko, Ming-Hsuan Yang, Tobias Weyand, Boqing Gong, Leonid Sigal |
| 2024 | Extensive-Form Game Solving via Blackwell Approachability on Treeplexes. Darshan Chakrabarti, Julien Grand-Clément, Christian Kroer |
| 2024 | Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data. Sofia Ek, Dave Zachariah |
| 2024 | Extracting Training Data from Molecular Pre-trained Models. Renhong Huang, Jiarong Xu, Zhiming Yang, Xiang Si, Xin Jiang, Hanyang Yuan, Chunping Wang, Yang Yang |
| 2024 | Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning. Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu, Xiang Li |
| 2024 | EyeGraph: Modularity-aware Spatio Temporal Graph Clustering for Continuous Event-based Eye Tracking. Nuwan Sriyantha Bandara, Thivya Kandappu, Argha Sen, Ila Gokarn, Archan Misra |
| 2024 | F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning. Huiping Zhuang, Yuchen Liu, Run He, Kai Tong, Ziqian Zeng, Cen Chen, Yi Wang, Lap-Pui Chau |
| 2024 | FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding? Marco Bornstein, Amrit Singh Bedi, Abdirisak Mohamed, Furong Huang |
| 2024 | FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification. Kexue Fu, Xiaoyuan Luo, Linhao Qu, Shuo Wang, Ying Xiong, Ilias Maglogiannis, Longxiang Gao, Manning Wang |
| 2024 | FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model. Xiaobao Wu, Thong Nguyen, Delvin Zhang, William Yang Wang, Anh Tuan Luu |
| 2024 | FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection. Jiaqi Wang, Xiaochen Wang, Lingjuan Lyu, Jinghui Chen, Fenglong Ma |
| 2024 | FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor. Mengting Xu, De Ma, Huajin Tang, Qian Zheng, Gang Pan |
| 2024 | FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning. Lisha Chen, A. F. M. Saif, Yanning Shen, Tianyi Chen |
| 2024 | FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors. Shuai Liu, Boyang Li, Zhiyu Fang, Mingyue Cui, Kai Huang |
| 2024 | FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction. Feijie Wu, Xingchen Wang, Yaqing Wang, Tianci Liu, Lu Su, Jing Gao |
| 2024 | FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation. Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo |
| 2024 | FIFO-Diffusion: Generating Infinite Videos from Text without Training. Jihwan Kim, Junoh Kang, Jinyoung Choi, Bohyung Han |
| 2024 | FINALLY: fast and universal speech enhancement with studio-like quality. Nicholas Babaev, Kirill Tamogashev, Azat Saginbaev, Ivan Shchekotov, Hanbin Bae, Hosang Sung, Won-Jun Lee, Hoon-Young Cho, Pavel Andreev |
| 2024 | FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models. Pengxiang Li, Zhi Gao, Bofei Zhang, Tao Yuan, Yuwei Wu, Mehrtash Harandi, Yunde Jia, Song-Chun Zhu, Qing Li |
| 2024 | FLAME : Factuality-Aware Alignment for Large Language Models. Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Scott Yih, Xilun Chen |
| 2024 | FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. Ziyao Wang, Zheyu Shen, Yexiao He, Guoheng Sun, Hongyi Wang, Lingjuan Lyu, Ang Li |
| 2024 | FM-Delta: Lossless Compression for Storing Massive Fine-tuned Foundation Models. Wanyi Ning, Jingyu Wang, Qi Qi, Mengde Zhu, Haifeng Sun, Daixuan Cheng, Jianxin Liao, Ce Zhang |
| 2024 | FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation. Kun Chen, Peng Ye, Hao Chen, Kang Chen, Tao Han, Wanli Ouyang, Tao Chen, Lei Bai |
| 2024 | FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection. Xinting Liao, Weiming Liu, Pengyang Zhou, Fengyuan Yu, Jiahe Xu, Jun Wang, Wenjie Wang, Chaochao Chen, Xiaolin Zheng |
| 2024 | FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler |
| 2024 | FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection. Austin Coursey, Junyi Ji, Marcos Quiñones-Grueiro, William Barbour, Yuhang Zhang, Tyler Derr, Gautam Biswas, Daniel B. Work |
| 2024 | FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features. Jitao Zhao, Di Jin, Meng Ge, Lianze Shan, Xin Wang, Dongxiao He, Zhiyong Feng |
| 2024 | FUGAL: Feature-fortified Unrestricted Graph Alignment. Aditya Bommakanti, Harshith Reddy Vonteri, Konstantinos Skitsas, Sayan Ranu, Davide Mottin, Panagiotis Karras |
| 2024 | FUSE: Fast Unified Simulation and Estimation for PDEs. Levi E. Lingsch, Dana Grund, Siddhartha Mishra, Georgios Kissas |
| 2024 | FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding. Shuai Yuan, Guancong Lin, Lixian Zhang, Runmin Dong, Jinxiao Zhang, Shuang Chen, Juepeng Zheng, Jie Wang, Haohuan Fu |
| 2024 | FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. Xiaohan Lin, Qingxing Cao, Yinya Huang, Haiming Wang, Jianqiao Lu, Zhengying Liu, Linqi Song, Xiaodan Liang |
| 2024 | Face2QR: A Unified Framework for Aesthetic, Face-Preserving, and Scannable QR Code Generation. Xuehao Cui, Guangyang Wu, Zhenghao Gan, Guangtao Zhai, Xiaohong Liu |
| 2024 | Facilitating Multimodal Classification via Dynamically Learning Modality Gap. Yang Yang, Fengqiang Wan, Qing-Yuan Jiang, Yi Xu |
| 2024 | FactorSim: Generative Simulation via Factorized Representation. Fan-Yun Sun, S. I. Harini, Angela Yi, Yihan Zhou, Alex Zook, Jonathan Tremblay, Logan Matthew Cross, Jiajun Wu, Nick Haber |
| 2024 | FactorizePhys: Matrix Factorization for Multidimensional Attention in Remote Physiological Sensing. Jitesh Joshi, Sos S. Agaian, Youngjun Cho |
| 2024 | Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation. Xin Yuan, Michael Maire |
| 2024 | Fair Allocation in Dynamic Mechanism Design. Alireza Fallah, Michael I. Jordan, Annie Ulichney |
| 2024 | Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium. Mehdi Yazdani-Jahromi, Ali Khodabandeh Yalabadi, Amirarsalan Rajabi, Aida Tayebi, Ivan Garibay, Ozlem O. Garibay |
| 2024 | Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior. Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra |
| 2024 | Fair Kernel K-Means: from Single Kernel to Multiple Kernel. Peng Zhou, Rongwen Li, Liang Du |
| 2024 | Fair Online Bilateral Trade. François Bachoc, Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni |
| 2024 | Fair Secretaries with Unfair Predictions. Eric Balkanski, Will Ma, Andreas Maggiori |
| 2024 | Fair Wasserstein Coresets. Zikai Xiong, Niccolò Dalmasso, Shubham Sharma, Freddy Lécué, Daniele Magazzeni, Vamsi K. Potluru, Tucker Balch, Manuela Veloso |
| 2024 | Fair and Welfare-Efficient Constrained Multi-Matchings under Uncertainty. Elita A. Lobo, Justin Payan, Cyrus Cousins, Yair Zick |
| 2024 | FairJob: A Real-World Dataset for Fairness in Online Systems. Mariia Vladimirova, Federico Pavone, Eustache Diemert |
| 2024 | FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models. Ruinan Jin, Zikang Xu, Yuan Zhong, Qingsong Yao, Qi Dou, S. Kevin Zhou, Xiaoxiao Li |
| 2024 | FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation. Christopher T. H. Teo, Milad Abdollahzadeh, Xinda Ma, Ngai-Man Cheung |
| 2024 | FairWire: Fair Graph Generation. Oyku Deniz Kose, Yanning Shen |
| 2024 | Fairness and Efficiency in Online Class Matching. MohammadTaghi Hajiaghayi, Shayan Chashm Jahan, Mohammad Sharifi, Suho Shin, Max Springer |
| 2024 | Fairness in Social Influence Maximization via Optimal Transport. Shubham Chowdhary, Giulia De Pasquale, Nicolas Lanzetti, Ana-Andreea Stoica, Florian Dörfler |
| 2024 | Fairness without Harm: An Influence-Guided Active Sampling Approach. Jinlong Pang, Jialu Wang, Zhaowei Zhu, Yuanshun Yao, Chen Qian, Yang Liu |
| 2024 | Fairness-Aware Estimation of Graphical Models. Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long, Li Shen |
| 2024 | Fairness-Aware Meta-Learning via Nash Bargaining. Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia |
| 2024 | FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings. Xiao Tan, Yiqin Wang, Yangyang Shen, Dian Shen, Meng Wang, Peibo Duan, Beilun Wang |
| 2024 | FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models. Rui Hu, Qian He, Gaofeng He, Jiedong Zhuang, Huang Chen, Huafeng Liu, Huamin Wang |
| 2024 | Fast Best-of-N Decoding via Speculative Rejection. Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter L. Bartlett, Andrea Zanette |
| 2024 | Fast Channel Simulation via Error-Correcting Codes. Sharang M. Sriramu, Rochelle Barsz, Elizabeth Polito, Aaron B. Wagner |
| 2024 | Fast Encoder-Based 3D from Casual Videos via Point Track Processing. Yoni Kasten, Wuyue Lu, Haggai Maron |
| 2024 | Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification. Yihong Luo, Yuhan Chen, Siya Qiu, Yiwei Wang, Chen Zhang, Yan Zhou, Xiaochun Cao, Jing Tang |
| 2024 | Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations. Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara |
| 2024 | Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms. Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-wei Lee, Haipeng Luo, Weiqiang Zheng |
| 2024 | Fast Proxy Experiment Design for Causal Effect Identification. Sepehr Elahi, Sina Akbari, Jalal Etesami, Negar Kiyavash, Patrick Thiran |
| 2024 | Fast Rates for Bandit PAC Multiclass Classification. Liad Erez, Alon Peled-Cohen, Tomer Koren, Yishay Mansour, Shay Moran |
| 2024 | Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets. Taira Tsuchiya, Shinji Ito |
| 2024 | Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time. Zixiang Chen, Huizhuo Yuan, Yongqian Li, Yiwen Kou, Junkai Zhang, Quanquan Gu |
| 2024 | Fast T2T: Optimization Consistency Speeds Up Diffusion-Based Training-to-Testing Solving for Combinatorial Optimization. Yang Li, Jinpei Guo, Runzhong Wang, Hongyuan Zha, Junchi Yan |
| 2024 | Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning. Aneesh Muppidi, Zhiyu Zhang, Heng Yang |
| 2024 | Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers. Krzysztof Marcin Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Han Lin, Kumar Avinava Dubey, Tamás Sarlós, Snigdha Chaturvedi |
| 2024 | Fast and Memory-Efficient Video Diffusion Using Streamlined Inference. Zheng Zhan, Yushu Wu, Yifan Gong, Zichong Meng, Zhenglun Kong, Changdi Yang, Geng Yuan, Pu Zhao, Wei Niu, Yanzhi Wang |
| 2024 | Fast samplers for Inverse Problems in Iterative Refinement models. Kushagra Pandey, Ruihan Yang, Stephan Mandt |
| 2024 | Fast yet Safe: Early-Exiting with Risk Control. Metod Jazbec, Alexander Timans, Tin Hadzi Veljkovic, Kaspar Sakmann, Dan Zhang, Christian Andersson Naesseth, Eric T. Nalisnick |
| 2024 | FastDrag: Manipulate Anything in One Step. Xuanjia Zhao, Jian Guan, Congyi Fan, Dongli Xu, Youtian Lin, Haiwei Pan, Pengming Feng |
| 2024 | FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models. Jiachang Liu, Rui Zhang, Cynthia Rudin |
| 2024 | Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints. Zhenwei Lin, Qi Deng |
| 2024 | Faster Algorithms for User-Level Private Stochastic Convex Optimization. Andrew Lowy, Daogao Liu, Hilal Asi |
| 2024 | Faster Differentially Private Top-k Selection: A Joint Exponential Mechanism with Pruning. Hao Wu, Hanwen Zhang |
| 2024 | Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference. Senmao Li, Taihang Hu, Joost van de Weijer, Fahad Shahbaz Khan, Tao Liu, Linxuan Li, Shiqi Yang, Yaxing Wang, Ming-Ming Cheng, Jian Yang |
| 2024 | Faster Local Solvers for Graph Diffusion Equations. Jiahe Bai, Baojian Zhou, Deqing Yang, Yanghua Xiao |
| 2024 | Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level. Ali Hassani, Wen-Mei Hwu, Humphrey Shi |
| 2024 | Faster Repeated Evasion Attacks in Tree Ensembles. Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis |
| 2024 | FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification. Jingfeng Yao, Cheng Wang, Wenyu Liu, Xinggang Wang |
| 2024 | Fearless Stochasticity in Expectation Propagation. Jonathan So, Richard E. Turner |
| 2024 | Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification. Xi Yang, Huanling Liu, De Cheng, Nannan Wang, Xinbo Gao |
| 2024 | FedAvP: Augment Local Data via Shared Policy in Federated Learning. Minui Hong, Junhyeog Yun, Insu Jeon, Gunhee Kim |
| 2024 | FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation. Jianqiao Zhang, Caifeng Shan, Jungong Han |
| 2024 | FedGMark: Certifiably Robust Watermarking for Federated Graph Learning. Yuxin Yang, Qiang Li, Yuan Hong, Binghui Wang |
| 2024 | FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning. Evelyn Ma, Chao Pan, S. Rasoul Etesami, Han Zhao, Olgica Milenkovic |
| 2024 | FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models. Rui Ye, Rui Ge, Xinyu Zhu, Jingyi Chai, Yaxin Du, Yang Liu, Yanfeng Wang, Siheng Chen |
| 2024 | FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation. Xiang Liu, Liangxi Liu, Feiyang Ye, Yunheng Shen, Xia Li, Linshan Jiang, Jialin Li |
| 2024 | FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction. Ziwei Li, Xiaoqi Wang, Hong-You Chen, Han-Wei Shen, Wei-Lun Chao |
| 2024 | FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference. Zihan Tan, Guancheng Wan, Wenke Huang, Mang Ye |
| 2024 | Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning. Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich |
| 2024 | Federated Black-Box Adaptation for Semantic Segmentation. Jay N. Paranjape, Shameema Sikder, S. Swaroop Vedula, Vishal M. Patel |
| 2024 | Federated Ensemble-Directed Offline Reinforcement Learning. Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil, Srinivas Shakkottai |
| 2024 | Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources. Jiamu Bai, Daoyuan Chen, Bingchen Qian, Liuyi Yao, Yaliang Li |
| 2024 | Federated Graph Learning for Cross-Domain Recommendation. Ziqi Yang, Zhaopeng Peng, Zihui Wang, Jianzhong Qi, Chaochao Chen, Weike Pan, Chenglu Wen, Cheng Wang, Xiaoliang Fan |
| 2024 | Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. Bikang Pan, Wei Huang, Ye Shi |
| 2024 | Federated Learning over Connected Modes. Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima |
| 2024 | Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. Michael Crawshaw, Mingrui Liu |
| 2024 | Federated Model Heterogeneous Matryoshka Representation Learning. Liping Yi, Han Yu, Chao Ren, Gang Wang, Xiaoguang Liu, Xiaoxiao Li |
| 2024 | Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning. Tong Yang, Shicong Cen, Yuting Wei, Yuxin Chen, Yuejie Chi |
| 2024 | Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups. Fengyu Gao, Ruiquan Huang, Jing Yang |
| 2024 | Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data. Zhaomin Wu, Junyi Hou, Yiqun Diao, Bingsheng He |
| 2024 | Feedback control guides credit assignment in recurrent neural networks. Klara Kaleb, Barbara Feulner, Juan Gallego, Claudia Clopath |
| 2024 | Feint Behaviors and Strategies: Formalization, Implementation and Evaluation. Junyu Liu, Xiangjun Peng |
| 2024 | Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity. Hanlin Gu, WinKent Ong, Chee Seng Chan, Lixin Fan |
| 2024 | Fetch and Forge: Efficient Dataset Condensation for Object Detection. Ding Qi, Jian Li, Jinlong Peng, Bo Zhao, Shuguang Dou, Jialin Li, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Cairong Zhao |
| 2024 | Few-Shot Adversarial Prompt Learning on Vision-Language Models. Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu |
| 2024 | Few-Shot Diffusion Models Escape the Curse of Dimensionality. Ruofeng Yang, Bo Jiang, Cheng Chen, Ruinan Jin, Baoxiang Wang, Shuai Li |
| 2024 | Few-Shot Task Learning through Inverse Generative Modeling. Aviv Netanyahu, Yilun Du, Antonia Bronars, Jyothish Pari, Josh Tenenbaum, Tianmin Shu, Pulkit Agrawal |
| 2024 | Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark. Brianna Karpowicz, Joel Ye, Chaofei Fan, Pablo Tostado-Marcos, Fabio Rizzoglio, Clayton Washington, Thiago Scodeler, Diogo de Lucena, Samuel R. Nason-Tomaszewski, Matthew Mender, Xuan Ma, Ezequiel M. Arneodo, Leigh R. Hochberg, Cynthia A. Chestek, Jaimie M. Henderson, Timothy Gentner, Vikash Gilja, Lee E. Miller, Adam Rouse, Robert Gaunt, Jennifer L. Collinger, Chethan Pandarinath |
| 2024 | FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training. Ruihong Yin, Vladimir Yugay, Yue Li, Sezer Karaoglu, Theo Gevers |
| 2024 | FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models. Tong Wu, Yinghao Xu, Ryan Po, Mengchen Zhang, Guandao Yang, Jiaqi Wang, Ziwei Liu, Dahua Lin, Gordon Wetzstein |
| 2024 | Fight Back Against Jailbreaking via Prompt Adversarial Tuning. Yichuan Mo, Yuji Wang, Zeming Wei, Yisen Wang |
| 2024 | FilterNet: Harnessing Frequency Filters for Time Series Forecasting. Kun Yi, Jingru Fei, Qi Zhang, Hui He, Shufeng Hao, Defu Lian, Wei Fan |
| 2024 | FinBen: A Holistic Financial Benchmark for Large Language Models. Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, Yijing Xu, Haoqiang Kang, Ziyan Kuang, Chenhan Yuan, Kailai Yang, Zheheng Luo, Tianlin Zhang, Zhiwei Liu, Guojun Xiong, Zhiyang Deng, Yuechen Jiang, Zhiyuan Yao, Haohang Li, Yangyang Yu, Gang Hu, Jiajia Huang, Xiao-Yang Liu, Alejandro Lopez-Lira, Benyou Wang, Yanzhao Lai, Hao Wang, Min Peng, Sophia Ananiadou, Jimin Huang |
| 2024 | FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making. Yangyang Yu, Zhiyuan Yao, Haohang Li, Zhiyang Deng, Yuechen Jiang, Yupeng Cao, Zhi Chen, Jordan W. Suchow, Zhenyu Cui, Rong Liu, Zhaozhuo Xu, Denghui Zhang, Koduvayur Subbalakshmi, Guojun Xiong, Yueru He, Jimin Huang, Dong Li, Qianqian Xie |
| 2024 | Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch |
| 2024 | Finding Transformer Circuits With Edge Pruning. Adithya Bhaskar, Alexander Wettig, Dan Friedman, Danqi Chen |
| 2024 | Finding good policies in average-reward Markov Decision Processes without prior knowledge. Adrienne Tuynman, Rémy Degenne, Emilie Kaufmann |
| 2024 | FindingEmo: An Image Dataset for Emotion Recognition in the Wild. Laurent P. Mertens, Elahe Yargholi, Hans P. Op de Beeck, Jan Van den Stock, Joost Vennekens |
| 2024 | Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination. Ruochen Liu, Hao Chen, Yuanchen Bei, Qijie Shen, Fangwei Zhong, Senzhang Wang, Jianxin Wang |
| 2024 | Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random. Mingming Ha, Taoxuewen, Wenfang Lin, Qiongxu Ma, Wujiang Xu, Linxun Chen |
| 2024 | Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning. Simon Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Peter Tong, Yifei Zhou, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, Sergey Levine |
| 2024 | Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. Youssef Allouah, Abdellah El Mrini, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot |
| 2024 | Fine-Tuning is Fine, if Calibrated. Zheda Mai, Arpita Chowdhury, Ping Zhang, Cheng-Hao Tu, Hong-You Chen, Vardaan Pahuja, Tanya Y. Berger-Wolf, Song Gao, Charles V. Stewart, Yu Su, Wei-Lun Chao |
| 2024 | Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond. Yingcong Li, Ankit Singh Rawat, Samet Oymak |
| 2024 | Fine-grained Control of Generative Data Augmentation in IoT Sensing. Tianshi Wang, Qikai Yang, Ruijie Wang, Dachun Sun, Jinyang Li, Yizhuo Chen, Yigong Hu, Chaoqi Yang, Tomoyoshi Kimura, Denizhan Kara, Tarek F. Abdelzaher |
| 2024 | Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models. Yifan Zhang, Junhui Hou |
| 2024 | FineCLIP: Self-distilled Region-based CLIP for Better Fine-grained Understanding. Dong Jing, Xiaolong He, Yutian Luo, Nanyi Fei, Guoxing Yang, Wei Wei, Huiwen Zhao, Zhiwu Lu |
| 2024 | FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models. Gong Zhang, Kihyuk Sohn, Meera Hahn, Humphrey Shi, Irfan Essa |
| 2024 | First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs. Ben Norman, Jeff Clune |
| 2024 | First-Order Methods for Linearly Constrained Bilevel Optimization. Guy Kornowski, Swati Padmanabhan, Kai Wang, Zhe Zhang, Suvrit Sra |
| 2024 | First-Order Minimax Bilevel Optimization. Yifan Yang, Zhaofeng Si, Siwei Lyu, Kaiyi Ji |
| 2024 | Fisher Flow Matching for Generative Modeling over Discrete Data. Oscar Davis, Samuel Kessler, Mircea Petrache, Ismail Ilkan Ceylan, Michael M. Bronstein, Avishek Joey Bose |
| 2024 | Fit for our purpose, not yours: Benchmark for a low-resource, Indigenous language. Suzanne Duncan, Gianna Leoni, Lee Steven, Keoni Mahelona, Peter-Lucas Jones |
| 2024 | Fixed Confidence Best Arm Identification in the Bayesian Setting. Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki |
| 2024 | FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision. Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao |
| 2024 | Flatten Anything: Unsupervised Neural Surface Parameterization. Qijian Zhang, Junhui Hou, Wenping Wang, Ying He |
| 2024 | Flaws can be Applause: Unleashing Potential of Segmenting Ambiguous Objects in SAM. Chenxin Li, Yuzhi Huang, Wuyang Li, Hengyu Liu, Xinyu Liu, Qing Xu, Zhen Chen, Yue Huang, Yixuan Yuan |
| 2024 | Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts. Sukwon Yun, Inyoung Choi, Jie Peng, Yangfan Wu, Jingxuan Bao, Qiyiwen Zhang, Jiayi Xin, Qi Long, Tianlong Chen |
| 2024 | FlexCap: Describe Anything in Images in Controllable Detail. Debidatta Dwibedi, Vidhi Jain, Jonathan Tompson, Andrew Zisserman, Yusuf Aytar |
| 2024 | FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning. Sizhe Liu, Jun Xia, Lecheng Zhang, Yuchen Liu, Yue Liu, Wenjie Du, Zhangyang Gao, Bozhen Hu, Cheng Tan, Hongxin Xiang, Stan Z. Li |
| 2024 | FlexPlanner: Flexible 3D Floorplanning via Deep Reinforcement Learning in Hybrid Action Space with Multi-Modality Representation. Ruizhe Zhong, Xingbo Du, Shixiong Kai, Zhentao Tang, Siyuan Xu, Jianye Hao, Mingxuan Yuan, Junchi Yan |
| 2024 | FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling. Zaixi Zhang, Mengdi Wang, Qi Liu |
| 2024 | Flexible Context-Driven Sensory Processing in Dynamical Vision Models. Lakshmi Narasimhan Govindarajan, Abhiram Iyer, Valmiki Kothare, Ila Fiete |
| 2024 | Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals. Abhiram Iyer, Sarthak Chandra, Sugandha Sharma, Ila Fiete |
| 2024 | Flexible task abstractions emerge in linear networks with fast and bounded units. Kai Sandbrink, Jan P. Bauer, Alexandra Maria Proca, Andrew M. Saxe, Christopher Summerfield, Ali Hummos |
| 2024 | Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery. Haonan Lin, Wenbin An, Jiahao Wang, Yan Chen, Feng Tian, Mengmeng Wang, Qianying Wang, Guang Dai, Jingdong Wang |
| 2024 | Flipping-based Policy for Chance-Constrained Markov Decision Processes. Xun Shen, Shuo Jiang, Akifumi Wachi, Kazumune Hashimoto, Sebastien Gros |
| 2024 | Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching. Yasi Zhang, Peiyu Yu, Yaxuan Zhu, Yingshan Chang, Feng Gao, Ying Nian Wu, Oscar Leong |
| 2024 | Flow Snapshot Neurons in Action: Deep Neural Networks Generalize to Biological Motion Perception. Shuangpeng Han, Ziyu Wang, Mengmi Zhang |
| 2024 | FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions. Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood |
| 2024 | FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner. Wenliang Zhao, Minglei Shi, Xumin Yu, Jie Zhou, Jiwen Lu |
| 2024 | Focus On What Matters: Separated Models For Visual-Based RL Generalization. Di Zhang, Bowen Lv, Hai Zhang, Feifan Yang, Junqiao Zhao, Hang Yu, Chang Huang, Hongtu Zhou, Chen Ye, Changjun Jiang |
| 2024 | Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning. Xinrui Wang, Chuanxing Geng, Wenhai Wan, Shao-Yuan Li, Songcan Chen |
| 2024 | FouRA: Fourier Low-Rank Adaptation. Shubhankar Borse, Shreya Kadambi, Nilesh Prasad Pandey, Kartikeya Bhardwaj, Viswanath Ganapathy, Sweta Priyadarshi, Risheek Garrepalli, Rafael Esteves, Munawar Hayat, Fatih Porikli |
| 2024 | Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding. Zhenyu Zhang, Runjin Chen, Shiwei Liu, Zhewei Yao, Olatunji Ruwase, Beidi Chen, Xiaoxia Wu, Zhangyang Wang |
| 2024 | Foundation Inference Models for Markov Jump Processes. David Berghaus, Kostadin Cvejoski, Patrick Seifner, César Ali Marin Ojeda, Ramsés J. Sánchez |
| 2024 | Foundations of Multivariate Distributional Reinforcement Learning. Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland |
| 2024 | Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting. Chiu Wai Yan, Shi Quan Foo, Van-Hoan Trinh, Dit-Yan Yeung, Ka-Hing Wong, Wai-Kin Wong |
| 2024 | Fourier-enhanced Implicit Neural Fusion Network for Multispectral and Hyperspectral Image Fusion. Yu-Jie Liang, Zihan Cao, Shangqi Deng, Hong-Xia Dou, Liang-Jian Deng |
| 2024 | Fractal Patterns May Illuminate the Success of Next-Token Prediction. Ibrahim M. Alabdulmohsin, Vinh Q. Tran, Mostafa Dehghani |
| 2024 | Free Lunch in Pathology Foundation Model: Task-specific Model Adaptation with Concept-Guided Feature Enhancement. Yanyan Huang, Weiqin Zhao, Yihang Chen, Yu Fu, Lequan Yu |
| 2024 | Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning. Mengmeng Chen, Xiaohu Wu, Xiaoli Tang, Tiantian He, Yew Soon Ong, Qiqi Liu, Qicheng Lao, Han Yu |
| 2024 | FreeLong: Training-Free Long Video Generation with SpectralBlend Temporal Attention. Yu Lu, Yuanzhi Liang, Linchao Zhu, Yi Yang |
| 2024 | FreeSplat: Generalizable 3D Gaussian Splatting Towards Free View Synthesis of Indoor Scenes. Yunsong Wang, Tianxin Huang, Hanlin Chen, Gim Hee Lee |
| 2024 | FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge. Hanzhe Li, Jiaran Zhou, Yuezun Li, Baoyuan Wu, Bin Li, Junyu Dong |
| 2024 | FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space. Yiyang Guo, Ruizhe Li, Mude Hui, Hanzhong Guo, Chen Zhang, Chuangjian Cai, Le Wan, Shangfei Wang |
| 2024 | Frequency Adaptive Normalization For Non-stationary Time Series Forecasting. Weiwei Ye, Songgaojun Deng, Qiaosha Zou, Ning Gui |
| 2024 | Frequency-aware Generative Models for Multivariate Time Series Imputation. Xinyu Yang, Yu Sun, Xiaojie Yuan, Xinyang Chen |
| 2024 | Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations. Alexander Tyurin, Kaja Gruntkowska, Peter Richtárik |
| 2024 | Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching. Yongqi Wang, Wenxiang Guo, Rongjie Huang, Jiawei Huang, Zehan Wang, Fuming You, Ruiqi Li, Zhou Zhao |
| 2024 | From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach. Timothée Devergne, Vladimir Kostic, Michele Parrinello, Massimiliano Pontil |
| 2024 | From Causal to Concept-Based Representation Learning. Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar |
| 2024 | From Chaos to Clarity: 3DGS in the Dark. Zhihao Li, Yufei Wang, Alex C. Kot, Bihan Wen |
| 2024 | From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement. Zhibin Gu, Songhe Feng |
| 2024 | From Instance Training to Instruction Learning: Task Adapters Generation from Instructions. Huanxuan Liao, Shizhu He, Yao Xu, Yuanzhe Zhang, Yanchao Hao, Shengping Liu, Kang Liu, Jun Zhao |
| 2024 | From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization. Mohammad Pedramfar, Vaneet Aggarwal |
| 2024 | From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection. Xinlei Wang, Maike Feng, Jing Qiu, Jinjin Gu, Junhua Zhao |
| 2024 | From Similarity to Superiority: Channel Clustering for Time Series Forecasting. Jialin Chen, Jan Eric Lenssen, Aosong Feng, Weihua Hu, Matthias Fey, Leandros Tassiulas, Jure Leskovec, Rex Ying |
| 2024 | From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning. Pusen Dong, Tianchen Zhu, Yue Qiu, Haoyi Zhou, Jianxin Li |
| 2024 | From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $\alpha$-NeuS. Haoran Zhang, Junkai Deng, Xuhui Chen, Fei Hou, Wencheng Wang, Hong Qin, Chen Qian, Ying He |
| 2024 | From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion Models. Zhuoshi Pan, Yuguang Yao, Gaowen Liu, Bingquan Shen, H. Vicky Zhao, Ramana Kompella, Sijia Liu |
| 2024 | From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When. Kevin Christian Wibisono, Yixin Wang |
| 2024 | From an Image to a Scene: Learning to Imagine the World from a Million 360° Videos. Matthew Wallingford, Anand Bhattad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi, Wei-Chiu Ma, Ali Farhadi |
| 2024 | Frozen-DETR: Enhancing DETR with Image Understanding from Frozen Foundation Models. Shenghao Fu, Junkai Yan, Qize Yang, Xihan Wei, Xiaohua Xie, Wei-Shi Zheng |
| 2024 | Frustratingly Easy Test-Time Adaptation of Vision-Language Models. Matteo Farina, Gianni Franchi, Giovanni Iacca, Massimiliano Mancini, Elisa Ricci |
| 2024 | Full-Atom Peptide Design with Geometric Latent Diffusion. Xiangzhe Kong, Yinjun Jia, Wenbing Huang, Yang Liu |
| 2024 | Full-Distance Evasion of Pedestrian Detectors in the Physical World. Zhi Cheng, Zhanhao Hu, Yuqiu Liu, Jianmin Li, Hang Su, Xiaolin Hu |
| 2024 | Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products. Bethia Sun, Maurice Pagnucco, Yang Song |
| 2024 | Fully Explicit Dynamic Gaussian Splatting. Junoh Lee, Changyeon Won, HyunJun Jung, Inhwan Bae, Hae-Gon Jeon |
| 2024 | Fully Unconstrained Online Learning. Ashok Cutkosky, Zakaria Mhammedi |
| 2024 | Functional Bilevel Optimization for Machine Learning. Ieva Petrulionyte, Julien Mairal, Michael Arbel |
| 2024 | Functional Gradient Flows for Constrained Sampling. Shiyue Zhang, Longlin Yu, Ziheng Cheng, Cheng Zhang |
| 2024 | Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem. Huaqing Zhang, Lesi Chen, Jing Xu, Jingzhao Zhang |
| 2024 | Fundamental Convergence Analysis of Sharpness-Aware Minimization. Pham Duy Khanh, Hoang-Chau Luong, Boris S. Mordukhovich, Dat Ba Tran |
| 2024 | Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models. Alliot Nagle, Adway Girish, Marco Bondaschi, Michael Gastpar, Ashok Vardhan Makkuva, Hyeji Kim |
| 2024 | FuseAnyPart: Diffusion-Driven Facial Parts Swapping via Multiple Reference Images. Zheng Yu, Yaohua Wang, Siying Cui, Aixi Zhang, Wei-Long Zheng, Senzhang Wang |
| 2024 | FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. Zhenheng Tang, Yonggang Zhang, Peijie Dong, Yiu-ming Cheung, Amelie Chi Zhou, Bo Han, Xiaowen Chu |
| 2024 | FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion. Xing Han, Huy Nguyen, Carl Harris, Nhat Ho, Suchi Saria |
| 2024 | G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering. Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi |
| 2024 | G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training. Che Liu, Cheng Ouyang, Sibo Cheng, Anand Shah, Wenjia Bai, Rossella Arcucci |
| 2024 | G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models. Pengyue Jia, Yiding Liu, Xiaopeng Li, Xiangyu Zhao, Yuhao Wang, Yantong Du, Xiao Han, Xuetao Wei, Shuaiqiang Wang, Dawei Yin |
| 2024 | GACL: Exemplar-Free Generalized Analytic Continual Learning. Huiping Zhuang, Yizhu Chen, Di Fang, Run He, Kai Tong, Hongxin Wei, Ziqian Zeng, Cen Chen |
| 2024 | GAIA: Rethinking Action Quality Assessment for AI-Generated Videos. Zijian Chen, Wei Sun, Yuan Tian, Jun Jia, Zicheng Zhang, Jiarui Wang, Ru Huang, Xiongkuo Min, Guangtao Zhai, Wen-Jun Zhang |
| 2024 | GAMap: Zero-Shot Object Goal Navigation with Multi-Scale Geometric-Affordance Guidance. Shuaihang Yuan, Hao Huang, Yu Hao, Congcong Wen, Anthony Tzes, Yi Fang |
| 2024 | GAVEL: Generating Games via Evolution and Language Models. Graham Todd, Alexander Padula, Matthew Stephenson, Éric Piette, Dennis J. N. J. Soemers, Julian Togelius |
| 2024 | GC-Bench: An Open and Unified Benchmark for Graph Condensation. Qingyun Sun, Ziying Chen, Beining Yang, Cheng Ji, Xingcheng Fu, Sheng Zhou, Hao Peng, Jianxin Li, Philip S. Yu |
| 2024 | GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning. Guibin Zhang, Haonan Dong, Yuchen Zhang, Zhixun Li, Dingshuo Chen, Kai Wang, Tianlong Chen, Yuxuan Liang, Dawei Cheng, Kun Wang |
| 2024 | GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics. Dominik Klein, Théo Uscidda, Fabian J. Theis, Marco Cuturi |
| 2024 | GFT: Graph Foundation Model with Transferable Tree Vocabulary. Zehong Wang, Zheyuan Zhang, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye |
| 2024 | GFlowNet Assisted Biological Sequence Editing. Pouya M. Ghari, Alex M. Tseng, Gökcen Eraslan, Romain Lopez, Tommaso Biancalani, Gabriele Scalia, Ehsan Hajiramezanali |
| 2024 | GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation. Junhao Cai, Yuji Yang, Weihao Yuan, Yisheng He, Zilong Dong, Liefeng Bo, Hui Cheng, Qifeng Chen |
| 2024 | GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning. Yanbin Wei, Shuai Fu, Weisen Jiang, Zejian Zhang, Zhixiong Zeng, Qi Wu, James T. Kwok, Yu Zhang |
| 2024 | GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration. Silong Yong, Yaqi Xie, Simon Stepputtis, Katia P. Sycara |
| 2024 | GLBench: A Comprehensive Benchmark for Graph with Large Language Models. Yuhan Li, Peisong Wang, Xiao Zhu, Aochuan Chen, Haiyun Jiang, Deng Cai, Wai Kin (Victor) Chan, Jia Li |
| 2024 | GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent. Hongtai Zeng, Chao Yang, Yanzhen Zhou, Cheng Yang, Qinglai Guo |
| 2024 | GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI. Pengcheng Chen, Jin Ye, Guoan Wang, Yanjun Li, Zhongying Deng, Wei Li, Tianbin Li, Haodong Duan, Ziyan Huang, Yanzhou Su, Benyou Wang, Shaoting Zhang, Bin Fu, Jianfei Cai, Bohan Zhuang, Eric J. Seibel, Junjun He, Yu Qiao |
| 2024 | GO4Align: Group Optimization for Multi-Task Alignment. Jiayi Shen, Qi Wang, Zehao Xiao, Nanne van Noord, Marcel Worring |
| 2024 | GOMAA-Geo: GOal Modality Agnostic Active Geo-localization. Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen, Chongjie Zhang, Nathan Jacobs, Yevgeniy Vorobeychik |
| 2024 | GRANOLA: Adaptive Normalization for Graph Neural Networks. Moshe Eliasof, Beatrice Bevilacqua, Carola-Bibiane Schönlieb, Haggai Maron |
| 2024 | GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models. Zaitang Li, Pin-Yu Chen, Tsung-Yi Ho |
| 2024 | GREATS: Online Selection of High-Quality Data for LLM Training in Every Iteration. Jiachen T. Wang, Tong Wu, Dawn Song, Prateek Mittal, Ruoxi Jia |
| 2024 | GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring. Dongwoo Lee, Joonkyu Park, Kyoung Mu Lee |
| 2024 | GS-Hider: Hiding Messages into 3D Gaussian Splatting. Xuanyu Zhang, Jiarui Meng, Runyi Li, Zhipei Xu, Yongbing Zhang, Jian Zhang |
| 2024 | GSDF: 3DGS Meets SDF for Improved Neural Rendering and Reconstruction. Mulin Yu, Tao Lu, Linning Xu, Lihan Jiang, Yuanbo Xiangli, Bo Dai |
| 2024 | GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats. Sangeek Hyun, Jae-Pil Heo |
| 2024 | GTA: A Benchmark for General Tool Agents. Jize Wang, Zerun Ma, Yining Li, Songyang Zhang, Cailian Chen, Kai Chen, Xinyi Le |
| 2024 | GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning. Jaewoo Lee, Sujin Yun, Taeyoung Yun, Jinkyoo Park |
| 2024 | GTBench: Uncovering the Strategic Reasoning Capabilities of LLMs via Game-Theoretic Evaluations. Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu |
| 2024 | GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks. Yu Zhang, Changhao Pan, Wenxiang Guo, Ruiqi Li, Zhiyuan Zhu, Jialei Wang, Wenhao Xu, Jingyu Lu, Zhiqing Hong, Chuxin Wang, Lichao Zhang, Jinzheng He, Ziyue Jiang, Yuxin Chen, Chen Yang, Jiecheng Zhou, Xinyu Cheng, Zhou Zhao |
| 2024 | GUIDE: Real-Time Human-Shaped Agents. Lingyu Zhang, Zhengran Ji, Nicholas R. Waytowich, Boyuan Chen |
| 2024 | GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning. Zehui Li, Vallijah Subasri, Guy-Bart Stan, Yiren Zhao, Bo Wang |
| 2024 | GVKF: Gaussian Voxel Kernel Functions for Highly Efficient Surface Reconstruction in Open Scenes. Gaochao Song, Chong Cheng, Hao Wang |
| 2024 | GameTraversalBenchmark: Evaluating Planning Abilities Of Large Language Models Through Traversing 2D Game Maps. Muhammad Umair Nasir, Steven James, Julian Togelius |
| 2024 | GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation. Haoran Lu, Ruihai Wu, Yitong Li, Sijie Li, Ziyu Zhu, Chuanruo Ning, Yan Zhao, Longzan Luo, Yuanpei Chen, Hao Dong |
| 2024 | Gated Inference Network: Inference and Learning State-Space Models. Hamidreza Hashempoorikderi, Wan Choi |
| 2024 | Gated Slot Attention for Efficient Linear-Time Sequence Modeling. Yu Zhang, Songlin Yang, Rui-Jie Zhu, Yue Zhang, Leyang Cui, Yiqiao Wang, Bolun Wang, Freda Shi, Bailin Wang, Wei Bi, Peng Zhou, Guohong Fu |
| 2024 | Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning. Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov |
| 2024 | Gaussian Graph Network: Learning Efficient and Generalizable Gaussian Representations from Multi-view Images. Shengjun Zhang, Xin Fei, Fangfu Liu, Haixu Song, Yueqi Duan |
| 2024 | Gaussian Process Bandits for Top-k Recommendations. Mohit Yadav, Cameron Musco, Daniel R. Sheldon |
| 2024 | GaussianCube: A Structured and Explicit Radiance Representation for 3D Generative Modeling. Bowen Zhang, Yiji Cheng, Jiaolong Yang, Chunyu Wang, Feng Zhao, Yansong Tang, Dong Chen, Baining Guo |
| 2024 | GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting. Umangi Jain, Ashkan Mirzaei, Igor Gilitschenski |
| 2024 | GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting. Xiufeng Huang, Ruiqi Li, Yiu-ming Cheung, Ka Chun Cheung, Simon See, Renjie Wan |
| 2024 | GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts. Deyu Zou, Shikun Liu, Siqi Miao, Victor Fung, Shiyu Chang, Pan Li |
| 2024 | GenAI Arena: An Open Evaluation Platform for Generative Models. Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen |
| 2024 | GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing. Zhenyu Wang, Aoxue Li, Zhenguo Li, Xihui Liu |
| 2024 | GenRL: Multimodal-foundation world models for generalization in embodied agents. Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Aaron C. Courville, Sai Rajeswar Mudumba |
| 2024 | GenRec: Unifying Video Generation and Recognition with Diffusion Models. Zejia Weng, Xitong Yang, Zhen Xing, Zuxuan Wu, Yu-Gang Jiang |
| 2024 | GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping. Junyoung Seo, Kazumi Fukuda, Takashi Shibuya, Takuya Narihira, Naoki Murata, Shoukang Hu, Chieh-Hsin Lai, Seungryong Kim, Yuki Mitsufuji |
| 2024 | Gene-Gene Relationship Modeling Based on Genetic Evidence for Single-Cell RNA-Seq Data Imputation. Daeho Um, Ji Won Yoon, Seong-Jin Ahn, Yunha Yeo |
| 2024 | General Articulated Objects Manipulation in Real Images via Part-Aware Diffusion Process. Zhou Fang, Yong-Lu Li, Lixin Yang, Cewu Lu |
| 2024 | General Detection-based Text Line Recognition. Raphaël Baena, Syrine Kalleli, Mathieu Aubry |
| 2024 | General bounds on the quality of Bayesian coresets. Trevor Campbell |
| 2024 | Generalizable Implicit Motion Modeling for Video Frame Interpolation. Zujin Guo, Wei Li, Chen Change Loy |
| 2024 | Generalizable Person Re-identification via Balancing Alignment and Uniformity. Yoonki Cho, Jaeyoon Kim, Woo Jae Kim, Junsik Jung, Sung-Eui Yoon |
| 2024 | Generalizable and Animatable Gaussian Head Avatar. Xuangeng Chu, Tatsuya Harada |
| 2024 | Generalizablity of Memorization Neural Network. Lijia Yu, Xiao-Shan Gao, Lijun Zhang, Yibo Miao |
| 2024 | Generalization Analysis for Label-Specific Representation Learning. Yifan Zhang, Min-Ling Zhang |
| 2024 | Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming. Shinsaku Sakaue, Taihei Oki |
| 2024 | Generalization Bounds via Conditional f-Information. Ziqiao Wang, Yongyi Mao |
| 2024 | Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure. Jin Zhang, Ze Liu, Defu Lian, Enhong Chen |
| 2024 | Generalization of Hamiltonian algorithms. Andreas Maurer |
| 2024 | Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts. Zhitong Gao, Bingnan Li, Mathieu Salzmann, Xuming He |
| 2024 | Generalized Eigenvalue Problems with Generative Priors. Zhaoqiang Liu, Wen Li, Junren Chen |
| 2024 | Generalized Fast Exact Conformalization. Diyang Li |
| 2024 | Generalized Linear Bandits with Limited Adaptivity. Ayush Sawarni, Nirjhar Das, Siddharth Barman, Gaurav Sinha |
| 2024 | Generalized Protein Pocket Generation with Prior-Informed Flow Matching. Zaixi Zhang, Marinka Zitnik, Qi Liu |
| 2024 | Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts. Andong Wang, Yuning Qiu, Mingyuan Bai, Zhong Jin, Guoxu Zhou, Qibin Zhao |
| 2024 | Generalizing CNNs to graphs with learnable neighborhood quantization. Isaac Osafo Nkansah, Neil Gallagher, Ruchi Sandilya, Conor Liston, Logan Grosenick |
| 2024 | Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization. Haoran Li, Zhennan Jiang, Yuhui Chen, Dongbin Zhao |
| 2024 | Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling. Wanghan Xu, Fenghua Ling, Wenlong Zhang, Tao Han, Hao Chen, Wanli Ouyang, Lei Bai |
| 2024 | Generate Universal Adversarial Perturbations for Few-Shot Learning. Yiman Hu, Yixiong Zou, Ruixuan Li, Yuhua Li |
| 2024 | Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation. Lili Wei, Congyan Lang, Ziyi Chen, Tao Wang, Yidong Li, Jun Liu |
| 2024 | Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search. Nicola Dainese, Matteo Merler, Minttu Alakuijala, Pekka Marttinen |
| 2024 | Generating Highly Designable Proteins with Geometric Algebra Flow Matching. Simon Wagner, Leif Seute, Vsevolod Viliuga, Nicolas Wolf, Frauke Gräter, Jan Stühmer |
| 2024 | Generating Origin-Destination Matrices in Neural Spatial Interaction Models. Ioannis Zachos, Mark Girolami, Theodoros Damoulas |
| 2024 | Generating compositional scenes via Text-to-image RGBA Instance Generation. Alessandro Fontanella, Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Sarah Parisot |
| 2024 | Generative Adversarial Model-Based Optimization via Source Critic Regularization. Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob R. Gardner, James C. Gee, Osbert Bastani |
| 2024 | Generative Forests. Richard Nock, Mathieu Guillame-Bert |
| 2024 | Generative Fractional Diffusion Models. Gabriel Nobis, Maximilian Springenberg, Marco Aversa, Michael Detzel, Rembert Daems, Roderick Murray-Smith, Shinichi Nakajima, Sebastian Lapuschkin, Stefano Ermon, Tolga Birdal, Manfred Opper, Christoph Knochenhauer, Luis Oala, Wojciech Samek |
| 2024 | Generative Hierarchical Materials Search. Sherry Yang, Simon L. Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo Jimenez Rezende, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk |
| 2024 | Generative Modeling of Molecular Dynamics Trajectories. Bowen Jing, Hannes Stärk, Tommi S. Jaakkola, Bonnie Berger |
| 2024 | Generative Modelling of Structurally Constrained Graphs. Manuel Madeira, Clément Vignac, Dorina Thanou, Pascal Frossard |
| 2024 | Generative Retrieval Meets Multi-Graded Relevance. Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng |
| 2024 | Generative Semi-supervised Graph Anomaly Detection. Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, Guansong Pang |
| 2024 | Genetic-guided GFlowNets for Sample Efficient Molecular Optimization. Hyeonah Kim, Minsu Kim, Sanghyeok Choi, Jinkyoo Park |
| 2024 | GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation. Chubin Zhang, Hongliang Song, Yi Wei, Chen Yu, Jiwen Lu, Yansong Tang |
| 2024 | GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields. Weiyi Xue, Zehan Zheng, Fan Lu, Haiyun Wei, Guang Chen, Changjun Jiang |
| 2024 | GeoPlant: Spatial Plant Species Prediction Dataset. Lukás Picek, Christophe Botella, Maximilien Servajean, César Leblanc, Rémi Palard, Théo Larcher, Benjamin Deneu, Diego Marcos, Pierre Bonnet, Alexis Joly |
| 2024 | Geodesic Optimization for Predictive Shift Adaptation on EEG data. Apolline Mellot, Antoine Collas, Sylvain Chevallier, Alexandre Gramfort, Denis A. Engemann |
| 2024 | Geometric Analysis of Nonlinear Manifold Clustering. Nimita Shinde, Tianjiao Ding, Daniel P. Robinson, René Vidal |
| 2024 | Geometric Exploitation for Indoor Panoramic Semantic Segmentation. Dinh Duc Cao, Seok Joon Kim, Kyusung Cho |
| 2024 | Geometric Trajectory Diffusion Models. Jiaqi Han, Minkai Xu, Aaron Lou, Haotian Ye, Stefano Ermon |
| 2024 | Geometric-Averaged Preference Optimization for Soft Preference Labels. Hiroki Furuta, Kuang-Huei Lee, Shixiang Shane Gu, Yutaka Matsuo, Aleksandra Faust, Heiga Zen, Izzeddin Gur |
| 2024 | Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures. Yadong Sun, Xiaofeng Cao, Yu Wang, Wei Ye, Jingcai Guo, Qing Guo |
| 2024 | Geometry Cloak: Preventing TGS-based 3D Reconstruction from Copyrighted Images. Qi Song, Ziyuan Luo, Ka Chun Cheung, Simon See, Renjie Wan |
| 2024 | Geometry of naturalistic object representations in recurrent neural network models of working memory. Xiaoxuan Lei, Takuya Ito, Pouya Bashivan |
| 2024 | Geometry-aware training of factorized layers in tensor Tucker format. Emanuele Zangrando, Steffen Schotthöfer, Gianluca Ceruti, Jonas Kusch, Francesco Tudisco |
| 2024 | Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework. Zhongchao Yi, Zhengyang Zhou, Qihe Huang, Yanjiang Chen, Liheng Yu, Xu Wang, Yang Wang |
| 2024 | Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning. Daniel Kunin, Allan Raventós, Clémentine C. J. Dominé, Feng Chen, David A. Klindt, Andrew M. Saxe, Surya Ganguli |
| 2024 | Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment. Jiaxiang Li, Siliang Zeng, Hoi-To Wai, Chenliang Li, Alfredo García, Mingyi Hong |
| 2024 | Gliding over the Pareto Front with Uniform Designs. Xiaoyuan Zhang, Genghui Li, Xi Lin, Yichi Zhang, Yifan Chen, Qingfu Zhang |
| 2024 | Global Convergence in Training Large-Scale Transformers. Cheng Gao, Yuan Cao, Zihao Li, Yihan He, Mengdi Wang, Han Liu, Jason M. Klusowski, Jianqing Fan |
| 2024 | Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems. Francisco Acosta, Fatih Dinc, William Redman, Manu S. Madhav, David A. Klindt, Nina Miolane |
| 2024 | Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers. Alberto Alfarano, François Charton, Amaury Hayat |
| 2024 | Global Rewards in Restless Multi-Armed Bandits. Naveen Raman, Zheyuan Shi, Fei Fang |
| 2024 | Globally Convergent Variational Inference. Declan McNamara, Jackson Loper, Jeffrey Regier |
| 2024 | Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing. Xixi Jia, Fangchen Feng, Deyu Meng, Defeng Sun |
| 2024 | GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages. Amir Hossein Kargaran, François Yvon, Hinrich Schütze |
| 2024 | GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching. Haibin He, Maoyuan Ye, Jing Zhang, Juhua Liu, Bo Du, Dacheng Tao |
| 2024 | Goal Conditioned Reinforcement Learning for Photo Finishing Tuning. Jiarui Wu, Yujin Wang, Lingen Li, Zhang Fan, Tianfan Xue |
| 2024 | Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning. Huzi Cheng, Joshua W. Brown |
| 2024 | Goal-Conditioned On-Policy Reinforcement Learning. Xudong Gong, Dawei Feng, Kele Xu, Bo Ding, Huaimin Wang |
| 2024 | Going Beyond Heuristics by Imposing Policy Improvement as a Constraint. Chi-Chang Lee, Zhang-Wei Hong, Pulkit Agrawal |
| 2024 | Gorilla: Large Language Model Connected with Massive APIs. Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez |
| 2024 | Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes. Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho |
| 2024 | Gradient Guidance for Diffusion Models: An Optimization Perspective. Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang |
| 2024 | Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints. Guanyu Nie, Vaneet Aggarwal, Christopher J. Quinn |
| 2024 | Gradient Rewiring for Editable Graph Neural Network Training. Zhimeng Jiang, Zirui Liu, Xiaotian Han, Qizhang Feng, Hongye Jin, Qiaoyu Tan, Kaixiong Zhou, Na Zou, Xia Ben Hu |
| 2024 | Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization. Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low |
| 2024 | Gradient-Variation Online Learning under Generalized Smoothness. Yan-Feng Xie, Peng Zhao, Zhi-Hua Zhou |
| 2024 | Gradient-based Discrete Sampling with Automatic Cyclical Scheduling. Patrick Pynadath, Riddhiman Bhattacharya, Arun Hariharan, Ruqi Zhang |
| 2024 | Gradient-free Decoder Inversion in Latent Diffusion Models. Seongmin Hong, Suh Yoon Jeon, Kyeonghyun Lee, Ernest K. Ryu, Se Young Chun |
| 2024 | Gradients of Functions of Large Matrices. Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy, Søren Hauberg |
| 2024 | Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization. Seyed Amir Saberi, Amir Najafi, Amin Behjati, Ala Emrani, Yasaman Zolfimoselo, Mahdi Shadrooy, Abolfazl S. Motahari, Babak H. Khalaj |
| 2024 | Grammar-Aligned Decoding. Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick, Nadia Polikarpova, Loris D'Antoni |
| 2024 | Graph Classification via Reference Distribution Learning: Theory and Practice. Zixiao Wang, Jicong Fan |
| 2024 | Graph Coarsening with Message-Passing Guarantees. Antonin Joly, Nicolas Keriven |
| 2024 | Graph Convolutions Enrich the Self-Attention in Transformers! Jeongwhan Choi, Hyowon Wi, Jayoung Kim, Yehjin Shin, Kookjin Lee, Nathaniel Trask, Noseong Park |
| 2024 | Graph Diffusion Policy Optimization. Yijing Liu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Wei Chen |
| 2024 | Graph Diffusion Transformers for Multi-Conditional Molecular Generation. Gang Liu, Jiaxin Xu, Tengfei Luo, Meng Jiang |
| 2024 | Graph Edit Distance with General Costs Using Neural Set Divergence. Eeshaan Jain, Indradyumna Roy, Saswat Meher, Soumen Chakrabarti, Abir De |
| 2024 | Graph Learning for Numeric Planning. Dillon Z. Chen, Sylvie Thiébaux |
| 2024 | Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series. Giangiacomo Mercatali, André Freitas, Jie Chen |
| 2024 | Graph Neural Networks Do Not Always Oversmooth. Bastian Epping, Alexandre René, Moritz Helias, Michael T. Schaub |
| 2024 | Graph Neural Networks Need Cluster-Normalize-Activate Modules. Arseny Skryagin, Felix Divo, Mohammad Amin Ali, Devendra Singh Dhami, Kristian Kersting |
| 2024 | Graph Neural Networks and Arithmetic Circuits. Timon Barlag, Vivian Holzapfel, Laura Strieker, Jonni Virtema, Heribert Vollmer |
| 2024 | Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention. Philipp Froehlich, Heinz Koeppl |
| 2024 | Graph neural networks and non-commuting operators. Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar |
| 2024 | Graph-based Uncertainty Metrics for Long-form Language Model Generations. Mingjian Jiang, Yangjun Ruan, Prasanna Sattigeri, Salim Roukos, Tatsunori B. Hashimoto |
| 2024 | Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models. Baao Xie, Qiuyu Chen, Yunnan Wang, Zequn Zhang, Xin Jin, Wenjun Zeng |
| 2024 | Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution. Cong Xu, Jun Wang, Jianyong Wang, Wei Zhang |
| 2024 | GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction. Shijin Duan, Ruyi Ding, Jiaxing He, Aidong Adam Ding, Yunsi Fei, Xiaolin Xu |
| 2024 | GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts. Shirley Wu, Kaidi Cao, Bruno Ribeiro, James Y. Zou, Jure Leskovec |
| 2024 | GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs. Zhao Zhang, Ziwei Zhao, Dong Wang, Liwei Wang |
| 2024 | GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules. Burouj Armgaan, Manthan Dalmia, Sourav Medya, Sayan Ranu |
| 2024 | GraphVis: Boosting LLMs with Visual Knowledge Graph Integration. Yihe Deng, Chenchen Ye, Zijie Huang, Mingyu Derek Ma, Yiwen Kou, Wei Wang |
| 2024 | Graphcode: Learning from multiparameter persistent homology using graph neural networks. Florian Russold, Michael Kerber |
| 2024 | Grasp as You Say: Language-guided Dexterous Grasp Generation. Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing, Xiantuo Tan, Xiao-Ming Wu, Hao Li, Mark R. Cutkosky, Wei-Shi Zheng |
| 2024 | Great Minds Think Alike: The Universal Convergence Trend of Input Salience. Yipei Wang, Jeffrey Siskind, Xiaoqian Wang |
| 2024 | Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting. Jiawei Xu, Zexin Fan, Jian Yang, Jin Xie |
| 2024 | Grokking of Implicit Reasoning in Transformers: A Mechanistic Journey to the Edge of Generalization. Boshi Wang, Xiang Yue, Yu Su, Huan Sun |
| 2024 | GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation. Yuseung Lee, Taehoon Yoon, Minhyuk Sung |
| 2024 | Grounded Answers for Multi-agent Decision-making Problem through Generative World Model. Zeyang Liu, Xinrui Yang, Shiguang Sun, Long Qian, Lipeng Wan, Xingyu Chen, Xuguang Lan |
| 2024 | Grounding Multimodal Large Language Models in Actions. Andrew Szot, Bogdan Mazoure, Harsh Agrawal, R. Devon Hjelm, Zsolt Kira, Alexander Toshev |
| 2024 | Group Robust Preference Optimization in Reward-free RLHF. Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas, Viraj Mehta, Pier Giuseppe Sessa, Haitham Bou-Ammar, Ilija Bogunovic |
| 2024 | Group and Shuffle: Efficient Structured Orthogonal Parametrization. Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim V. Rakhuba |
| 2024 | Group-wise oracle-efficient algorithms for online multi-group learning. Samuel Deng, Jingwen Liu, Daniel J. Hsu |
| 2024 | GuardT2I: Defending Text-to-Image Models from Adversarial Prompts. Yijun Yang, Ruiyuan Gao, Xiao Yang, Jianyuan Zhong, Qiang Xu |
| 2024 | Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization. Taeyoung Yun, Sujin Yun, Jaewoo Lee, Jinkyoo Park |
| 2024 | Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame. Evan Markou, Thalaiyasingam Ajanthan, Stephen Gould |
| 2024 | Guiding a Diffusion Model with a Bad Version of Itself. Tero Karras, Miika Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, Samuli Laine |
| 2024 | HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection. Yuxin Wang, Duanyu Feng, Yongfu Dai, Zhengyu Chen, Jimin Huang, Sophia Ananiadou, Qianqian Xie, Hao Wang |
| 2024 | HAWK: Learning to Understand Open-World Video Anomalies. Jiaqi Tang, Hao Lu, Ruizheng Wu, Xiaogang Xu, Ke Ma, Cheng Fang, Bin Guo, Jiangbo Lu, Qifeng Chen, Yingcong Chen |
| 2024 | HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning. Lu Bai, Zhuo Xu, Lixin Cui, Ming Li, Yue Wang, Edwin R. Hancock |
| 2024 | HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting. Yuanhao Cai, Zihao Xiao, Yixun Liang, Minghan Qin, Yulun Zhang, Xiaokang Yang, Yaoyao Liu, Alan L. Yuille |
| 2024 | HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data. Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik |
| 2024 | HEMM: Holistic Evaluation of Multimodal Foundation Models. Paul Pu Liang, Akshay Goindani, Talha Chafekar, Leena Mathur, Haofei Yu, Ruslan Salakhutdinov, Louis-Philippe Morency |
| 2024 | HENASY: Learning to Assemble Scene-Entities for Interpretable Egocentric Video-Language Model. Khoa Vo, Thinh Phan, Kashu Yamazaki, Minh Tran, Ngan Le |
| 2024 | HEPrune: Fast Private Training of Deep Neural Networks With Encrypted Data Pruning. Yancheng Zhang, Mengxin Zheng, Yuzhang Shang, Xun Chen, Qian Lou |
| 2024 | HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis. Guillaume Jaume, Paul Doucet, Andrew H. Song, Ming Yang Lu, Cristina Almagro-Pérez, Sophia J. Wagner, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Ahrong Kim, Faisal Mahmood |
| 2024 | HGDL: Heterogeneous Graph Label Distribution Learning. Yufei Jin, Heng Lian, Yi He, Xingquan Zhu |
| 2024 | HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems. Hao Xu, Jia Pan |
| 2024 | HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction. Qianyue Hao, Jingyang Fan, Fengli Xu, Jian Yuan, Yong Li |
| 2024 | HOI-Swap: Swapping Objects in Videos with Hand-Object Interaction Awareness. Zihui Xue, Romy Luo, Changan Chen, Kristen Grauman |
| 2024 | HOPE: Shape Matching Via Aligning Different K-hop Neighbourhoods. Barakeel Fanseu Kamhoua, Huamin Qu |
| 2024 | HORSE: Hierarchical Representation for Large-Scale Neural Subset Selection. Binghui Xie, Yixuan Wang, Yongqiang Chen, Kaiwen Zhou, Yu Li, Wei Meng, James Cheng |
| 2024 | HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models. Rhea Sanjay Sukthanker, Arber Zela, Benedikt Staffler, Aaron Klein, Lennart Purucker, Jörg K. H. Franke, Frank Hutter |
| 2024 | HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning. Momin Ahmad Khan, Yasra Chandio, Fatima M. Anwar |
| 2024 | HYDRA: Model Factorization Framework for Black-Box LLM Personalization. Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, Bo Dai |
| 2024 | HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis. Shraddha Barke, Emmanuel Anaya Gonzalez, Saketh Ram Kasibatla, Taylor Berg-Kirkpatrick, Nadia Polikarpova |
| 2024 | HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion. Yu Zeng, Yang Zhang, Jiachen Liu, Linlin Shen, Kaijun Deng, Weizhao He, Jinbao Wang |
| 2024 | HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach. Maxim Nikolaev, Mikhail Kuznetsov, Dmitry P. Vetrov, Aibek Alanov |
| 2024 | Hallo3D: Multi-Modal Hallucination Detection and Mitigation for Consistent 3D Content Generation. Hongbo Wang, Jie Cao, Jin Liu, Xiaoqiang Zhou, Huaibo Huang, Ran He |
| 2024 | HaloScope: Harnessing Unlabeled LLM Generations for Hallucination Detection. Xuefeng Du, Chaowei Xiao, Sharon Li |
| 2024 | Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba. Haoye Dong, Aviral Chharia, Wenbo Gou, Francisco Vicente Carrasco, Fernando De la Torre |
| 2024 | Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models. Jinlin Lai, Justin Domke, Daniel R. Sheldon |
| 2024 | Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient. Vu C. Dinh, Lam S. Ho, Cuong V. Nguyen |
| 2024 | Hamiltonian Score Matching and Generative Flows. Peter Holderrieth, Yilun Xu, Tommi S. Jaakkola |
| 2024 | Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. Peng Tan, Hai-Tian Liu, Zhi-Hao Tan, Zhi-Hua Zhou |
| 2024 | Happy: A Debiased Learning Framework for Continual Generalized Category Discovery. Shijie Ma, Fei Zhu, Zhun Zhong, Wenzhuo Liu, Xu-Yao Zhang, Chenglin Liu |
| 2024 | HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation. Joseph Cotnareanu, Zhanguang Zhang, Hui-Ling Zhen, Yingxue Zhang, Mark Coates |
| 2024 | Hardness of Learning Neural Networks under the Manifold Hypothesis. Bobak T. Kiani, Jason Wang, Melanie Weber |
| 2024 | Harmonizing Stochasticity and Determinism: Scene-responsive Diverse Human Motion Prediction. Zhenyu Lou, Qiongjie Cui, Tuo Wang, Zhenbo Song, Luoming Zhang, Cheng Cheng, Haofan Wang, Xu Tang, Huaxia Li, Hong Zhou |
| 2024 | Harmonizing Visual Text Comprehension and Generation. Zhen Zhao, Jingqun Tang, Binghong Wu, Chunhui Lin, Shu Wei, Hao Liu, Xin Tan, Zhizhong Zhang, Can Huang, Yuan Xie |
| 2024 | Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions. Rawal Khirodkar, Jyun-Ting Song, Jinkun Cao, Zhengyi Luo, Kris Kitani |
| 2024 | Harnessing Multiple Correlated Networks for Exact Community Recovery. Miklós Z. Rácz, Jifan Zhang |
| 2024 | Harnessing small projectors and multiple views for efficient vision pretraining. Arna Ghosh, Kumar Krishna Agrawal, Shagun Sodhani, Adam Oberman, Blake A. Richards |
| 2024 | Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models. Frederik Kunstner, Alan Milligan, Robin Yadav, Mark Schmidt, Alberto Bietti |
| 2024 | HelpSteer 2: Open-source dataset for training top-performing reward models. Zhilin Wang, Yi Dong, Olivier Delalleau, Jiaqi Zeng, Gerald Shen, Daniel Egert, Jimmy Zhang, Makesh Narsimhan Sreedhar, Oleksii Kuchaiev |
| 2024 | Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning. Huancheng Chen, Haris Vikalo |
| 2024 | HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation. Bo Cheng, Yuhang Ma, Liebucha Wu, Shanyuan Liu, Ao Ma, Xiaoyu Wu, Dawei Leng, Yuhui Yin |
| 2024 | HiCoM: Hierarchical Coherent Motion for Dynamic Streamable Scenes with 3D Gaussian Splatting. Qiankun Gao, Jiarui Meng, Chengxiang Wen, Jie Chen, Jian Zhang |
| 2024 | Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models. Arshia Hemmat, Adam Davies, Tom A. Lamb, Jianhao Yuan, Philip Torr, Ashkan Khakzar, Francesco Pinto |
| 2024 | Hierarchical Federated Learning with Multi-Timescale Gradient Correction. Wenzhi Fang, Dong-Jun Han, Evan Chen, Shiqiang Wang, Christopher G. Brinton |
| 2024 | Hierarchical Hybrid Sliced Wasserstein: A Scalable Metric for Heterogeneous Joint Distributions. Khai Nguyen, Nhat Ho |
| 2024 | Hierarchical Object-Aware Dual-Level Contrastive Learning for Domain Generalized Stereo Matching. Yikun Miao, Meiqing Wu, Siew Kei Lam, Changsheng Li, Thambipillai Srikanthan |
| 2024 | Hierarchical Programmatic Option Framework. Yu-An Lin, Chen-Tao Lee, Chih-Han Yang, Guan-Ting Liu, Shao-Hua Sun |
| 2024 | Hierarchical Selective Classification. Shani Goren, Ido Galil, Ran El-Yaniv |
| 2024 | Hierarchical Uncertainty Exploration via Feedforward Posterior Trees. Elias Nehme, Rotem Mulayoff, Tomer Michaeli |
| 2024 | Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding. Jaeyoo Park, Jin Young Choi, JeongHyung Park, Bohyung Han |
| 2024 | Hierarchical and Density-based Causal Clustering. Kwangho Kim, Jisu Kim, Larry A. Wasserman, Edward H. Kennedy |
| 2024 | Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic Image Structure. Simone Rossetti, Fiora Pirri |
| 2024 | High Rank Path Development: an approach to learning the filtration of stochastic processes. Jiajie Tao, Hao Ni, Chong Liu |
| 2024 | High-Resolution Image Harmonization with Adaptive-Interval Color Transformation. Quanling Meng, Qinglin Liu, Zonglin Li, Xiangyuan Lan, Shengping Zhang, Liqiang Nie |
| 2024 | High-dimensional (Group) Adversarial Training in Linear Regression. Yiling Xie, Xiaoming Huo |
| 2024 | High-probability complexity bounds for stochastic non-convex minimax optimization. Yassine Laguel, Yasa Syed, Necdet Serhat Aybat, Mert Gürbüzbalaban |
| 2024 | Higher-Order Causal Message Passing for Experimentation with Complex Interference. Mohsen Bayati, Yuwei Luo, William Overman, Mohamad Sadegh Shirani Faradonbeh, Ruoxuan Xiong |
| 2024 | Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama, Federico Errica, Henrik Christiansen, Makoto Takamoto, Nicolas Weber, Mathias Niepert |
| 2024 | Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation. Nachiket Kotalwar, Alkis Gotovos, Adish Singla |
| 2024 | HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models. Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, Yu Su |
| 2024 | Historical Test-time Prompt Tuning for Vision Foundation Models. Jingyi Zhang, Jiaxing Huang, Xiaoqin Zhang, Ling Shao, Shijian Lu |
| 2024 | Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models. Wonguk Cho, Seokeon Choi, Debasmit Das, Matthias Reisser, Taesup Kim, Sungrack Yun, Fatih Porikli |
| 2024 | Homology Consistency Constrained Efficient Tuning for Vision-Language Models. Huatian Zhang, Lei Zhang, Yongdong Zhang, Zhendong Mao |
| 2024 | HonestLLM: Toward an Honest and Helpful Large Language Model. Chujie Gao, Siyuan Wu, Yue Huang, Dongping Chen, Qihui Zhang, Zhengyan Fu, Yao Wan, Lichao Sun, Xiangliang Zhang |
| 2024 | Honor Among Bandits: No-Regret Learning for Online Fair Division. Ariel D. Procaccia, Ben Schiffer, Shirley Zhang |
| 2024 | HourVideo: 1-Hour Video-Language Understanding. Keshigeyan Chandrasegaran, Agrim Gupta, Lea M. Hadzic, Taran Kota, Jimming He, Cristóbal Eyzaguirre, Zane Durante, Manling Li, Jiajun Wu, Li Fei-Fei |
| 2024 | How Control Information Influences Multilingual Text Image Generation and Editing? Boqiang Zhang, Zuan Gao, Yadong Qu, Hongtao Xie |
| 2024 | How Diffusion Models Learn to Factorize and Compose. Qiyao Liang, Ziming Liu, Mitchell Ostrow, Ila Fiete |
| 2024 | How Do Large Language Models Acquire Factual Knowledge During Pretraining? Hoyeon Chang, Jinho Park, Seonghyeon Ye, Sohee Yang, Youngkyung Seo, Du-Seong Chang, Minjoon Seo |
| 2024 | How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization? Jun Chen, Hong Chen, Bin Gu |
| 2024 | How Does Message Passing Improve Collaborative Filtering? Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye, Yozen Liu, Neil Shah, Tong Zhao |
| 2024 | How Does Variance Shape the Regret in Contextual Bandits? Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei |
| 2024 | How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad. Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Colin Sandon, Omid Saremi |
| 2024 | How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks. Etai Littwin, Omid Saremi, Madhu Advani, Vimal Thilak, Preetum Nakkiran, Chen Huang, Joshua M. Susskind |
| 2024 | How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval. Philip Fradkin, Puria Azadi Moghadam, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini |
| 2024 | How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective. Qiaozhe Zhang, Ruijie Zhang, Jun Sun, Yingzhuang Liu |
| 2024 | How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression. Xingwu Chen, Lei Zhao, Difan Zou |
| 2024 | How do Large Language Models Handle Multilingualism? Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, Lidong Bing |
| 2024 | How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers. Xin Lu, Yanyan Zhao, Bing Qin, Liangyu Huo, Qing Yang, Dongliang Xu |
| 2024 | How does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks. Mo Zhou, Rong Ge |
| 2024 | How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach. Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli |
| 2024 | How does PDE order affect the convergence of PINNs? Changhoon Song, Yesom Park, Myungjoo Kang |
| 2024 | How many classifiers do we need? Hyunsuk Kim, Liam Hodgkinson, Ryan Theisen, Michael W. Mahoney |
| 2024 | How to Boost Any Loss Function. Richard Nock, Yishay Mansour |
| 2024 | How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization? Jiahua Dong, Wenqi Liang, Hongliu Li, Duzhen Zhang, Meng Cao, Henghui Ding, Salman H. Khan, Fahad Shahbaz Khan |
| 2024 | How to Solve Contextual Goal-Oriented Problems with Offline Datasets? Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng |
| 2024 | How to Use Diffusion Priors under Sparse Views? Qisen Wang, Yifan Zhao, Jiawei Ma, Jia Li |
| 2024 | HuRef: HUman-REadable Fingerprint for Large Language Models. Boyi Zeng, Lizheng Wang, Yuncong Hu, Yi Xu, Chenghu Zhou, Xinbing Wang, Yu Yu, Zhouhan Lin |
| 2024 | Human Expertise in Algorithmic Prediction. Rohan Alur, Manish Raghavan, Devavrat Shah |
| 2024 | Human-3Diffusion: Realistic Avatar Creation via Explicit 3D Consistent Diffusion Models. Yuxuan Xue, Xianghui Xie, Riccardo Marin, Gerard Pons-Moll |
| 2024 | Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions. Heng Li, Minghan Li, Zhi-Qi Cheng, Yifei Dong, Yuxuan Zhou, Jun-Yan He, Qi Dai, Teruko Mitamura, Alexander G. Hauptmann |
| 2024 | Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models. Liulei Li, Wenguan Wang, Yi Yang |
| 2024 | HumanSplat: Generalizable Single-Image Human Gaussian Splatting with Structure Priors. Panwang Pan, Zhuo Su, Chenguo Lin, Zhen Fan, Yongjie Zhang, Zeming Li, Tingting Shen, Yadong Mu, Yebin Liu |
| 2024 | HumanVLA: Towards Vision-Language Directed Object Rearrangement by Physical Humanoid. Xinyu Xu, Yizheng Zhang, Yonglu Li, Lei Han, Cewu Lu |
| 2024 | HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation. Zhenzhi Wang, Yixuan Li, Yanhong Zeng, Youqing Fang, Yuwei Guo, Wenran Liu, Jing Tan, Kai Chen, Tianfan Xue, Bo Dai, Dahua Lin |
| 2024 | Humanoid Locomotion as Next Token Prediction. Ilija Radosavovic, Bike Zhang, Baifeng Shi, Jathushan Rajasegaran, Sarthak Kamat, Trevor Darrell, Koushil Sreenath, Jitendra Malik |
| 2024 | Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning. Jifan Zhang, Lalit K. Jain, Yang Guo, Jiayi Chen, Kuan Lok Zhou, Siddharth Suresh, Andrew Wagenmaker, Scott Sievert, Timothy T. Rogers, Kevin Jamieson, Bob Mankoff, Robert Nowak |
| 2024 | Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability. Nina Gubina, Andrei Dmitrenko, Gleb V. Solovev, Lyubov Yamshchikova, Oleg Petrov, Ivan Lebedev, Nikita Serov, Grigorii Kirgizov, Nikolay O. Nikitin, Vladimir Vinogradov |
| 2024 | Hybrid Mamba for Few-Shot Segmentation. Qianxiong Xu, Xuanyi Liu, Lanyun Zhu, Guosheng Lin, Cheng Long, Ziyue Li, Rui Zhao |
| 2024 | Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs. Kevin Tan, Wei Fan, Yuting Wei |
| 2024 | Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models. Sujai Hiremath, Jacqueline R. M. A. Maasch, Mengxiao Gao, Promit Ghosal, Kyra Gan |
| 2024 | Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers. Sukjun Hwang, Aakash Sunil Lahoti, Ratish Puduppully, Tri Dao, Albert Gu |
| 2024 | HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning. Chunlin Tian, Zhan Shi, Zhijiang Guo, Li Li, Cheng-Zhong Xu |
| 2024 | HydraViT: Stacking Heads for a Scalable ViT. Janek Haberer, Ali Hojjat, Olaf Landsiedel |
| 2024 | Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis. Yuxi Ren, Xin Xia, Yanzuo Lu, Jiacheng Zhang, Jie Wu, Pan Xie, Xing Wang, Xuefeng Xiao |
| 2024 | Hyper-opinion Evidential Deep Learning for Out-of-Distribution Detection. Jingen Qu, Yufei Chen, Xiaodong Yue, Wei Fu, Qiguang Huang |
| 2024 | HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets. Yang Yang, Wendi Ren, Shuang Li |
| 2024 | HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links. Haizhou Du, Yijian Chen, Ryan Yang, Yuchen Li, Linghe Kong |
| 2024 | Hyperbolic Embeddings of Supervised Models. Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth |
| 2024 | Hypothesis Testing the Circuit Hypothesis in LLMs. Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei |
| 2024 | I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token. Roi Cohen, Konstantin Dobler, Eden Biran, Gerard de Melo |
| 2024 | I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing. Yiwei Ma, Jiayi Ji, Ke Ye, Weihuang Lin, Zhibin Wang, Yonghan Zheng, Qiang Zhou, Xiaoshuai Sun, Rongrong Ji |
| 2024 | ID Jianqing Xu, Shen Li, Jiaying Wu, Miao Xiong, Ailin Deng, Jiazhen Ji, Yuge Huang, Guodong Mu, Wenjie Feng, Shouhong Ding, Bryan Hooi |
| 2024 | ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling. Francesca Babiloni, Alexandros Lattas, Jiankang Deng, Stefanos Zafeiriou |
| 2024 | IDGen: Item Discrimination Induced Prompt Generation for LLM Evaluation. Fan Lin, Shuyi Xie, Yong Dai, Wenlin Yao, Tianjiao Lang, Yu Zhang |
| 2024 | IF-Font: Ideographic Description Sequence-Following Font Generation. Xinping Chen, Xiao Ke, Wenzhong Guo |
| 2024 | II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models. Ziqiang Liu, Feiteng Fang, Xi Feng, Xeron Du, Chenhao Zhang, Noah Wang, Yuelin Bai, Qixuan Zhao, Liyang Fan, Chengguang Gan, Hongquan Lin, Jiaming Li, Yuansheng Ni, Haihong Wu, Yaswanth Narsupalli, Zhigang Zheng, Chengming Li, Xiping Hu, Ruifeng Xu, Xiaojun Chen, Min Yang, Jiaheng Liu, Ruibo Liu, Wenhao Huang, Ge Zhang, Shiwen Ni |
| 2024 | IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videos. Yunong Liu, Cristóbal Eyzaguirre, Manling Li, Shubh Khanna, Juan Carlos Niebles, Vineeth Ravi, Saumitra Mishra, Weiyu Liu, Jiajun Wu |
| 2024 | IMAGPose: A Unified Conditional Framework for Pose-Guided Person Generation. Fei Shen, Jinhui Tang |
| 2024 | IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization. Xiaochen Ma, Xuekang Zhu, Lei Su, Bo Du, Zhuohang Jiang, Bingkui Tong, Zeyu Lei, Xinyu Yang, Chi-Man Pun, Jiancheng Lv, Jizhe Zhou |
| 2024 | IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design Patents. Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi, Sourav Medya |
| 2024 | INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness. Hung Le, Doyen Sahoo, Yingbo Zhou, Caiming Xiong, Silvio Savarese |
| 2024 | INQUIRE: A Natural World Text-to-Image Retrieval Benchmark. Edward Vendrow, Omiros Pantazis, Alexander Shepard, Gabriel J. Brostow, Kate E. Jones, Oisin Mac Aodha, Sara Beery, Grant Van Horn |
| 2024 | IODA: Instance-Guided One-shot Domain Adaptation for Super-Resolution. Zaizuo Tang, Yu-Bin Yang |
| 2024 | IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs. Xi Gao, Jinxin Xiong, Akang Wang, Qihong Duan, Jiang Xue, Qingjiang Shi |
| 2024 | IPO: Interpretable Prompt Optimization for Vision-Language Models. Yingjun Du, Wenfang Sun, Cees Snoek |
| 2024 | IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering. Ruosen Li, Ruochen Li, Barry Wang, Xinya Du |
| 2024 | IR-CM: The Fast and General-purpose Image Restoration Method Based on Consistency Model. Xiaoxuan Gong, Jie Ma |
| 2024 | IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neurons. Dan Shi, Renren Jin, Tianhao Shen, Weilong Dong, Xinwei Wu, Deyi Xiong |
| 2024 | IWBVT: Instance Weighting-based Bias-Variance Trade-off for Crowdsourcing. Wenjun Zhang, Liangxiao Jiang, Chaoqun Li |
| 2024 | IaC-Eval: A Code Generation Benchmark for Cloud Infrastructure-as-Code Programs. Patrick Tser Jern Kon, Jiachen Liu, Yiming Qiu, Weijun Fan, Ting He, Lei Lin, Haoran Zhang, Owen Park, George Elengikal, Yuxin Kang, Ang Chen, Mosharaf Chowdhury, Myungjin Lee, Xinyu Wang |
| 2024 | Identifiability Analysis of Linear ODE Systems with Hidden Confounders. Yuanyuan Wang, Biwei Huang, Wei Huang, Xi Geng, Mingming Gong |
| 2024 | Identifiability Guarantees for Causal Disentanglement from Purely Observational Data. Ryan Welch, Jiaqi Zhang, Caroline Uhler |
| 2024 | Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention. Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca Toni, Ben Glocker, Fabio De Sousa Ribeiro |
| 2024 | Identifiable Shared Component Analysis of Unpaired Multimodal Mixtures. Subash Timilsina, Sagar Shrestha, Xiao Fu |
| 2024 | Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments. Feng Xie, Zhen Yao, Lin Xie, Yan Zeng, Zhi Geng |
| 2024 | Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees. Negin Musavi, Ziyao Guo, Geir E. Dullerud, Yingying Li |
| 2024 | Identify Then Recommend: Towards Unsupervised Group Recommendation. Yue Liu, Shihao Zhu, Tianyuan Yang, Jian Ma, Wenliang Zhong |
| 2024 | Identifying Causal Effects Under Functional Dependencies. Yizuo Chen, Adnan Darwiche |
| 2024 | Identifying Equivalent Training Dynamics. William T. Redman, Juan M. Bello-Rivas, Maria Fonoberova, Ryan Mohr, Yannis G. Kevrekidis, Igor Mezic |
| 2024 | Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning. Dan Braun, Jordan Taylor, Nicholas Goldowsky-Dill, Lee Sharkey |
| 2024 | Identifying General Mechanism Shifts in Linear Causal Representations. Tianyu Chen, Kevin Bello, Francesco Locatello, Bryon Aragam, Pradeep Ravikumar |
| 2024 | Identifying Latent State-Transition Processes for Individualized Reinforcement Learning. Yuewen Sun, Biwei Huang, Yu Yao, Donghuo Zeng, Xinshuai Dong, Songyao Jin, Boyang Sun, Roberto Legaspi, Kazushi Ikeda, Peter Spirtes, Kun Zhang |
| 2024 | Identifying Selections for Unsupervised Subtask Discovery. Yiwen Qiu, Yujia Zheng, Kun Zhang |
| 2024 | Identifying Spatio-Temporal Drivers of Extreme Events. Mohamad Hakam Shams Eddin, Jürgen Gall |
| 2024 | Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model. Min Zhao, Hongzhou Zhu, Chendong Xiang, Kaiwen Zheng, Chongxuan Li, Jun Zhu |
| 2024 | Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models. Sangwon Jang, Jaehyeong Jo, Kimin Lee, Sung Ju Hwang |
| 2024 | Idiographic Personality Gaussian Process for Psychological Assessment. Yehu Chen, Muchen Xi, Joshua Jackson, Jacob M. Montgomery, Roman Garnett |
| 2024 | If You Want to Be Robust, Be Wary of Initialization. Sofiane Ennadir, Johannes F. Lutzeyer, Michalis Vazirgiannis, El Houcine Bergou |
| 2024 | IllumiNeRF: 3D Relighting Without Inverse Rendering. Xiaoming Zhao, Pratul P. Srinivasan, Dor Verbin, Keunhong Park, Ricardo Martin-Brualla, Philipp Henzler |
| 2024 | ImOV3D: Learning Open Vocabulary Point Clouds 3D Object Detection from Only 2D Images. Timing Yang, Yuanliang Ju, Li Yi |
| 2024 | Image Copy Detection for Diffusion Models. Wenhao Wang, Yifan Sun, Zhentao Tan, Yi Yang |
| 2024 | Image Reconstruction Via Autoencoding Sequential Deep Image Prior. Ismail Alkhouri, Shijun Liang, Evan Bell, Qing Qu, Rongrong Wang, Saiprasad Ravishankar |
| 2024 | Image Textualization: An Automatic Framework for Generating Rich and Detailed Image Descriptions. Renjie Pi, Jianshu Zhang, Jipeng Zhang, Rui Pan, Zhekai Chen, Tong Zhang |
| 2024 | Image Understanding Makes for A Good Tokenizer for Image Generation. Luting Wang, Yang Zhao, Zijian Zhang, Jiashi Feng, Si Liu, Bingyi Kang |
| 2024 | Image-aware Evaluation of Generated Medical Reports. Gefen Dawidowicz, Elad Hirsch, Ayellet Tal |
| 2024 | Image2Struct: Benchmarking Structure Extraction for Vision-Language Models. Josselin Somerville Roberts, Tony Lee, Chi Heem Wong, Michihiro Yasunaga, Yifan Mai, Percy Liang |
| 2024 | ImageNet3D: Towards General-Purpose Object-Level 3D Understanding. Wufei Ma, Guofeng Zhang, Qihao Liu, Guanning Zeng, Adam Kortylewski, Yaoyao Liu, Alan L. Yuille |
| 2024 | Images that Sound: Composing Images and Sounds on a Single Canvas. Ziyang Chen, Daniel Geng, Andrew Owens |
| 2024 | Imitating Language via Scalable Inverse Reinforcement Learning. Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin A. Riedmiller |
| 2024 | Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment. Yiheng Li, Heyang Jiang, Akio Kodaira, Masayoshi Tomizuka, Kurt Keutzer, Chenfeng Xu |
| 2024 | Implicit Bias of Mirror Flow on Separable Data. Scott Pesme, Radu-Alexandru Dragomir, Nicolas Flammarion |
| 2024 | Implicit Curriculum in Procgen Made Explicit. Zhenxiong Tan, Kaixin Wang, Xinchao Wang |
| 2024 | Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs. Mustafa Shukor, Matthieu Cord |
| 2024 | Implicit Optimization Bias of Next-token Prediction in Linear Models. Christos Thrampoulidis |
| 2024 | Implicit Regularization Paths of Weighted Neural Representations. Jin-Hong Du, Pratik Patil |
| 2024 | Implicit Regularization of Decentralized Gradient Descent for Sparse Regression. Tongle Wu, Ying Sun |
| 2024 | Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems. Bingcong Li, Liang Zhang, Niao He |
| 2024 | Implicit Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenes. Qi Ma, Danda Pani Paudel, Ender Konukoglu, Luc Van Gool |
| 2024 | Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient. Natasa Tagasovska, Vladimir Gligorijevic, Kyunghyun Cho, Andreas Loukas |
| 2024 | Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xiang Li, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj |
| 2024 | Improved Algorithms for Contextual Dynamic Pricing. Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet |
| 2024 | Improved Analysis for Bandit Learning in Matching Markets. Fang Kong, Zilong Wang, Shuai Li |
| 2024 | Improved Bayes Regret Bounds for Multi-Task Hierarchical Bayesian Bandit Algorithms. Jiechao Guan, Hui Xiong |
| 2024 | Improved Distribution Matching Distillation for Fast Image Synthesis. Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, Bill Freeman |
| 2024 | Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses. Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Jing Jiang, Min Lin |
| 2024 | Improved Generation of Adversarial Examples Against Safety-aligned LLMs. Qizhang Li, Yiwen Guo, Wangmeng Zuo, Hao Chen |
| 2024 | Improved Guarantees for Fully Dynamic k-Center Clustering with Outliers in General Metric Spaces. Leyla Biabani, Annika Hennes, Denise La Gordt Dillie, Morteza Monemizadeh, Melanie Schmidt |
| 2024 | Improved Particle Approximation Error for Mean Field Neural Networks. Atsushi Nitanda |
| 2024 | Improved Regret for Bandit Convex Optimization with Delayed Feedback. Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang |
| 2024 | Improved Regret of Linear Ensemble Sampling. Harin Lee, Min-hwan Oh |
| 2024 | Improved Sample Complexity Bounds for Diffusion Model Training. Shivam Gupta, Aditya Parulekar, Eric Price, Zhiyang Xun |
| 2024 | Improved Sample Complexity for Multiclass PAC Learning. Steve Hanneke, Shay Moran, Qian Zhang |
| 2024 | Improved learning rates in multi-unit uniform price auctions. Marius Potfer, Dorian Baudry, Hugo Richard, Vianney Perchet, Cheng Wan |
| 2024 | Improved off-policy training of diffusion samplers. Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin |
| 2024 | Improving Adaptivity via Over-Parameterization in Sequence Models. Yicheng Li, Qian Lin |
| 2024 | Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation. Shiji Zhao, Ranjie Duan, Xizhe Wang, Xingxing Wei |
| 2024 | Improving Alignment and Robustness with Circuit Breakers. Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, J. Zico Kolter, Matt Fredrikson, Dan Hendrycks |
| 2024 | Improving Context-Aware Preference Modeling for Language Models. Silviu Pitis, Ziang Xiao, Nicolas Le Roux, Alessandro Sordoni |
| 2024 | Improving Decision Sparsity. Yiyang Sun, Tong Wang, Cynthia Rudin |
| 2024 | Improving Deep Learning Optimization through Constrained Parameter Regularization. Jörg K. H. Franke, Michael Hefenbrock, Gregor Köhler, Frank Hutter |
| 2024 | Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn. Hongyao Tang, Glen Berseth |
| 2024 | Improving Environment Novelty Quantification for Effective Unsupervised Environment Design. Jayden Teoh, Wenjun Li, Pradeep Varakantham |
| 2024 | Improving Equivariant Model Training via Constraint Relaxation. Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Shubhendu Trivedi, Kostas Daniilidis |
| 2024 | Improving Generalization and Convergence by Enhancing Implicit Regularization. Mingze Wang, Jinbo Wang, Haotian He, Zilin Wang, Guanhua Huang, Feiyu Xiong, Zhiyu Li, Weinan E, Lei Wu |
| 2024 | Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach. Hao Zhang, Chenglin Li, Nuowen Kan, Ziyang Zheng, Wenrui Dai, Junni Zou, Hongkai Xiong |
| 2024 | Improving Generalization of Dynamic Graph Learning via Environment Prompt. Kuo Yang, Zhengyang Zhou, Qihe Huang, Limin Li, Yuxuan Liang, Yang Wang |
| 2024 | Improving Gloss-free Sign Language Translation by Reducing Representation Density. Jinhui Ye, Xing Wang, Wenxiang Jiao, Junwei Liang, Hui Xiong |
| 2024 | Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato |
| 2024 | Improving Neural Network Surface Processing with Principal Curvatures. Josquin Harrison, James Benn, Maxime Sermesant |
| 2024 | Improving Neural ODE Training with Temporal Adaptive Batch Normalization. Su Zheng, Zhengqi Gao, Fan-Keng Sun, Duane S. Boning, Bei Yu, Martin D. F. Wong |
| 2024 | Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective. Yibo Miao, Yinpeng Dong, Jinlai Zhang, Lijia Yu, Xiao Yang, Xiao-Shan Gao |
| 2024 | Improving Sparse Decomposition of Language Model Activations with Gated Sparse Autoencoders. Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, Neel Nanda |
| 2024 | Improving Subgroup Robustness via Data Selection. Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry |
| 2024 | Improving Temporal Link Prediction via Temporal Walk Matrix Projection. Xiaodong Lu, Leilei Sun, Tongyu Zhu, Weifeng Lv |
| 2024 | Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection. Yinxuan Huang, Chengmin Gao, Bin Li, Xiangyang Xue |
| 2024 | Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition. Mengke Li, Ye Liu, Yang Lu, Yiqun Zhang, Yiu-ming Cheung, Hui Huang |
| 2024 | Improving robustness to corruptions with multiplicative weight perturbations. Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski |
| 2024 | Improving self-training under distribution shifts via anchored confidence with theoretical guarantees. Taejong Joo, Diego Klabjan |
| 2024 | Improving the Learning Capability of Small-size Image Restoration Network by Deep Fourier Shifting. Man Zhou |
| 2024 | Improving the Training of Rectified Flows. Sangyun Lee, Zinan Lin, Giulia Fanti |
| 2024 | Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity. Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik |
| 2024 | In Pursuit of Causal Label Correlations for Multi-label Image Recognition. Zhao-Min Chen, Xin Jin, Yisu Ge, Sixian Chan |
| 2024 | In-Context Learning State Vector with Inner and Momentum Optimization. Dongfang Li, Zhenyu Liu, Xinshuo Hu, Zetian Sun, Baotian Hu, Min Zhang |
| 2024 | In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization. Ruiqi Zhang, Jingfeng Wu, Peter L. Bartlett |
| 2024 | In-Context Learning with Representations: Contextual Generalization of Trained Transformers. Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi |
| 2024 | In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, Sanjay Shakkottai |
| 2024 | In-Context Symmetries: Self-Supervised Learning through Contextual World Models. Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi S. Jaakkola, Stefanie Jegelka |
| 2024 | In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment. Dongting Hu, Huan Fu, Jiaxian Guo, Liuhua Peng, Tingjin Chu, Feng Liu, Tongliang Liu, Mingming Gong |
| 2024 | In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates. Shicheng Liu, Minghui Zhu |
| 2024 | In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies. Yunbum Kook, Santosh S. Vempala, Matthew Shunshi Zhang |
| 2024 | Incentivizing Quality Text Generation via Statistical Contracts. Eden Saig, Ohad Einav, Inbal Talgam-Cohen |
| 2024 | IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark. Fredrik D. Johansson |
| 2024 | Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques. Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Nghia Hoang |
| 2024 | Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery. Yongwei Nie, Mingxian Fan, Chengjiang Long, Qing Zhang, Jian Zhu, Xuemiao Xu |
| 2024 | Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation. Daehee Lee, Minjong Yoo, Woo Kyung Kim, Wonje Choi, Honguk Woo |
| 2024 | IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS. Ashwin Sankar, Srija Anand, Praveen Srinivasa Varadhan, Sherry Thomas, Mehak Singal, Shridhar Kumar, Deovrat Mehendale, Aditi Krishana, Giri Raju, Mitesh M. Khapra |
| 2024 | Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities. Prasenjit Karmakar, Swadhin Pradhan, Sandip Chakraborty |
| 2024 | Induced Model Matching: Restricted Models Help Train Full-Featured Models. Usama Muneeb, Mesrob I. Ohannessian |
| 2024 | Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse. Samuel Lippl, Jack W. Lindsey |
| 2024 | Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models. Zhengmian Hu, Heng Huang |
| 2024 | Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence. Feng-Yi Liao, Lijun Ding, Yang Zheng |
| 2024 | InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory. Chaojun Xiao, Pengle Zhang, Xu Han, Guangxuan Xiao, Yankai Lin, Zhengyan Zhang, Zhiyuan Liu, Maosong Sun |
| 2024 | Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline. Qi Jia, Baoyu Fan, Cong Xu, Lu Liu, Liang Jin, Guoguang Du, Zhenhua Guo, Yaqian Zhao, Xuanjing Huang, Rengang Li |
| 2024 | Inference of Neural Dynamics Using Switching Recurrent Neural Networks. Yongxu Zhang, Shreya Saxena |
| 2024 | Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference. Benjamin Eysenbach, Vivek Myers, Ruslan Salakhutdinov, Sergey Levine |
| 2024 | Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors. Chao Chen, Yu-Shen Liu, Zhizhong Han |
| 2024 | Inferring stochastic low-rank recurrent neural networks from neural data. Matthijs Pals, A Erdem Sagtekin, Felix Pei, Manuel Glöckler, Jakob H. Macke |
| 2024 | InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models. Linyi Li, Shijie Geng, Zhenwen Li, Yibo He, Hao Yu, Ziyue Hua, Guanghan Ning, Siwei Wang, Tao Xie, Hongxia Yang |
| 2024 | Infinite Limits of Multi-head Transformer Dynamics. Blake Bordelon, Hamza Tahir Chaudhry, Cengiz Pehlevan |
| 2024 | Infinite-Dimensional Feature Interaction. Chenhui Xu, Fuxun Yu, Maoliang Li, Zihao Zheng, Zirui Xu, Jinjun Xiong, Xiang Chen |
| 2024 | Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models. Daniela de Albuquerque, John M. Pearson |
| 2024 | InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling. Yuchun Miao, Sen Zhang, Liang Ding, Rong Bao, Lefei Zhang, Dacheng Tao |
| 2024 | Information Re-Organization Improves Reasoning in Large Language Models. Xiaoxia Cheng, Zeqi Tan, Wei Xue, Weiming Lu |
| 2024 | Information-theoretic Generalization Analysis for Expected Calibration Error. Futoshi Futami, Masahiro Fujisawa |
| 2024 | Information-theoretic Limits of Online Classification with Noisy Labels. Changlong Wu, Ananth Grama, Wojciech Szpankowski |
| 2024 | Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models. Zun Wang, Chang Liu, Nianlong Zou, He Zhang, Xinran Wei, Lin Huang, Lijun Wu, Bin Shao |
| 2024 | Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation. Sagi Eppel, Jolina Li, Manuel S. Drehwald, Alán Aspuru-Guzik |
| 2024 | Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing. Zhongwang Zhang, Pengxiao Lin, Zhiwei Wang, Yaoyu Zhang, Zhi-Qin John Xu |
| 2024 | Initializing Services in Interactive ML Systems for Diverse Users. Avinandan Bose, Mihaela Curmei, Daniel L. Jiang, Jamie H. Morgenstern, Sarah Dean, Lillian J. Ratliff, Maryam Fazel |
| 2024 | Initializing Variable-sized Vision Transformers from Learngene with Learnable Transformation. Shiyu Xia, Yuankun Zu, Xu Yang, Xin Geng |
| 2024 | Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models. Alkis Kalavasis, Amin Karbasi, Argyris Oikonomou, Katerina Sotiraki, Grigoris Velegkas, Manolis Zampetakis |
| 2024 | Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space. Maximilian Stölzle, Cosimo Della Santina |
| 2024 | Instance-Optimal Private Density Estimation in the Wasserstein Distance. Vitaly Feldman, Audra McMillan, Satchit Sivakumar, Kunal Talwar |
| 2024 | Instance-Specific Asymmetric Sensitivity in Differential Privacy. David Durfee |
| 2024 | Instance-adaptive Zero-shot Chain-of-Thought Prompting. Xiaosong Yuan, Chen Shen, Shaotian Yan, Xiaofeng Zhang, Liang Xie, Wenxiao Wang, Renchu Guan, Ying Wang, Jieping Ye |
| 2024 | InstructG2I: Synthesizing Images from Multimodal Attributed Graphs. Bowen Jin, Ziqi Pang, Bingjun Guo, Yu-Xiong Wang, Jiaxuan You, Jiawei Han |
| 2024 | Instruction Embedding: Latent Representations of Instructions Towards Task Identification. Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Boyuan Pan, Heda Wang, Yao Hu, Kan Li |
| 2024 | Instruction Tuning Large Language Models to Understand Electronic Health Records. Zhenbang Wu, Anant Dadu, Mike A. Nalls, Faraz Faghri, Jimeng Sun |
| 2024 | Instruction Tuning With Loss Over Instructions. Zhengxiang Shi, Adam X. Yang, Bin Wu, Laurence Aitchison, Emine Yilmaz, Aldo Lipani |
| 2024 | Instruction-Guided Visual Masking. Jinliang Zheng, Jianxiong Li, Sijie Cheng, Yinan Zheng, Jiaming Li, Jihao Liu, Yu Liu, Jingjing Liu, Xianyuan Zhan |
| 2024 | Instructor-inspired Machine Learning for Robust Molecular Property Prediction. Fang Wu, Shuting Jin, Siyuan Li, Stan Z. Li |
| 2024 | Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation. Arvind Vepa, Zukang Yang, Andrew Choi, Jungseock Joo, Fabien Scalzo, Yizhou Sun |
| 2024 | Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion. Masahito Uwamichi, Simon K. Schnyder, Tetsuya J. Kobayashi, Satoshi Sawai |
| 2024 | Integrating Suboptimal Human Knowledge with Hierarchical Reinforcement Learning for Large-Scale Multiagent Systems. Dingbang Liu, Shohei Kato, Wen Gu, Fenghui Ren, Jun Yan, Guoxin Su |
| 2024 | InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint. Zhenzhi Wang, Jingbo Wang, Yixuan Li, Dahua Lin, Bo Dai |
| 2024 | InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object Interaction. Sirui Xu, Ziyin Wang, Yu-Xiong Wang, Liangyan Gui |
| 2024 | Interaction-Force Transport Gradient Flows. Egor Gladin, Pavel E. Dvurechenskii, Alexander Mielke, Jia-Jie Zhu |
| 2024 | Interactive Deep Clustering via Value Mining. Honglin Liu, Peng Hu, Changqing Zhang, Yunfan Li, Xi Peng |
| 2024 | Interfacing Foundation Models' Embeddings. Xueyan Zou, Linjie Li, Jianfeng Wang, Jianwei Yang, Mingyu Ding, Junyi Wei, Zhengyuan Yang, Feng Li, Hao Zhang, Shilong Liu, Arul Aravinthan, Yong Jae Lee, Lijuan Wang |
| 2024 | InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD. Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Songyang Zhang, Haodong Duan, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Zhe Chen, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Kai Chen, Conghui He, Xingcheng Zhang, Jifeng Dai, Yu Qiao, Dahua Lin, Jiaqi Wang |
| 2024 | InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques. Rohan Gupta, Iván Arcuschin Moreno, Thomas Kwa, Adrià Garriga-Alonso |
| 2024 | Interpolating Item and User Fairness in Multi-Sided Recommendations. Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei, Djallel Bouneffouf |
| 2024 | Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification. Zhaorui Tan, Xi Yang, Qiufeng Wang, Anh Nguyen, Kaizhu Huang |
| 2024 | Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents. Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel, Wolfgang Stammer, Kristian Kersting |
| 2024 | Interpretable Concept-Based Memory Reasoning. David Debot, Pietro Barbiero, Francesco Giannini, Gabriele Ciravegna, Michelangelo Diligenti, Giuseppe Marra |
| 2024 | Interpretable Generalized Additive Models for Datasets with Missing Values. Hayden McTavish, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin |
| 2024 | Interpretable Image Classification with Adaptive Prototype-based Vision Transformers. Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen |
| 2024 | Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors. Viet Ho Tam Thuc Do, Parham Eftekhar, Seyed Alireza Hosseini, Gene Cheung, Philip A. Chou |
| 2024 | Interpretable Mesomorphic Networks for Tabular Data. Arlind Kadra, Sebastian Pineda-Arango, Josif Grabocka |
| 2024 | Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE). Usha Bhalla, Alex Oesterling, Suraj Srinivas, Flávio P. Calmon, Himabindu Lakkaraju |
| 2024 | Interpreting Learned Feedback Patterns in Large Language Models. Luke Marks, Amir Abdullah, Clement Neo, Rauno Arike, David Krueger, Philip Torr, Fazl Barez |
| 2024 | Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge. Fawaz Sammani, Nikos Deligiannis |
| 2024 | Interpreting the Weight Space of Customized Diffusion Models. Amil Dravid, Yossi Gandelsman, Kuan-Chieh Wang, Rameen Abdal, Gordon Wetzstein, Alexei A. Efros, Kfir Aberman |
| 2024 | Intervention and Conditioning in Causal Bayesian Networks. Sainyam Galhotra, Joseph Y. Halpern |
| 2024 | Interventional Causal Discovery in a Mixture of DAGs. Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer |
| 2024 | Interventionally Consistent Surrogates for Complex Simulation Models. Joel Dyer, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Anisoara Calinescu, Theodoros Damoulas, Michael J. Wooldridge |
| 2024 | IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors. Shenghe Zheng, Hongzhi Wang, Xianglong Liu |
| 2024 | Intrinsic Robustness of Prophet Inequality to Strategic Reward Signaling. Wei Tang, Haifeng Xu, Ruimin Zhang, Derek Zhu |
| 2024 | Intrinsic Self-Supervision for Data Quality Audits. Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Álvaro González-Jiménez, Ludovic Amruthalingam, Matthew Groh, Alexander A. Navarini, Marc Pouly |
| 2024 | Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting. Bong Gyun Kang, Dongjun Lee, HyunGi Kim, Dohyun Chung, Sungroh Yoon |
| 2024 | Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity. Kaiqu Liang, Zixu Zhang, Jaime F. Fisac |
| 2024 | Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level. Runlin Lei, Yuwei Hu, Yuchen Ren, Zhewei Wei |
| 2024 | Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation. Keqiang Yan, Xiner Li, Hongyi Ling, Kenna Ashen, Carl Edwards, Raymundo Arróyave, Marinka Zitnik, Heng Ji, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji |
| 2024 | Invariant subspaces and PCA in nearly matrix multiplication time. Aleksandros Sobczyk, Marko Mladenovic, Mathieu Luisier |
| 2024 | Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning. The Viet Bui, Tien Anh Mai, Thanh Hong Nguyen |
| 2024 | Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions. Hideaki Kim |
| 2024 | Inversion-based Latent Bayesian Optimization. Jaewon Chu, Jinyoung Park, Seunghun Lee, Hyunwoo J. Kim |
| 2024 | InversionView: A General-Purpose Method for Reading Information from Neural Activations. Xinting Huang, Madhur Panwar, Navin Goyal, Michael Hahn |
| 2024 | Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps. Nikita Starodubcev, Mikhail Khoroshikh, Artem Babenko, Dmitry Baranchuk |
| 2024 | Invisible Image Watermarks Are Provably Removable Using Generative AI. Xuandong Zhao, Kexun Zhang, Zihao Su, Saastha Vasan, Ilya Grishchenko, Christopher Kruegel, Giovanni Vigna, Yu-Xiang Wang, Lei Li |
| 2024 | Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models. Jiayu Wang, Yifei Ming, Zhenmei Shi, Vibhav Vineet, Xin Wang, Sharon Li, Neel Joshi |
| 2024 | Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning. Dylan J. Foster, Adam Block, Dipendra Misra |
| 2024 | Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance? Garud Iyengar, Henry Lam, Tianyu Wang |
| 2024 | Is Function Similarity Over-Engineered? Building a Benchmark. Rebecca Saul, Chang Liu, Noah Fleischmann, Richard Zak, Kristopher K. Micinski, Edward Raff, James Holt |
| 2024 | Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions. Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang |
| 2024 | Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning? Yang Dai, Oubo Ma, Longfei Zhang, Xingxing Liang, Shengchao Hu, Mengzhu Wang, Shouling Ji, Jincai Huang, Li Shen |
| 2024 | Is Multiple Object Tracking a Matter of Specialization? Gianluca Mancusi, Mattia Bernardi, Aniello Panariello, Angelo Porrello, Rita Cucchiara, Simone Calderara |
| 2024 | Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL. Enoch H. Kang, P. R. Kumar |
| 2024 | Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models. Athanasios Tragakis, Marco Aversa, Chaitanya Kaul, Roderick Murray-Smith, Daniele Faccio |
| 2024 | Is Programming by Example Solved by LLMs? Wen-Ding Li, Kevin Ellis |
| 2024 | Is Score Matching Suitable for Estimating Point Processes? Haoqun Cao, Zizhuo Meng, Tianjun Ke, Feng Zhou |
| 2024 | Is Value Learning Really the Main Bottleneck in Offline RL? Seohong Park, Kevin Frans, Sergey Levine, Aviral Kumar |
| 2024 | Is Your HD Map Constructor Reliable under Sensor Corruptions? Xiaoshuai Hao, Mengchuan Wei, Yifan Yang, Haimei Zhao, Hui Zhang, Yi Zhou, Qiang Wang, Weiming Li, Lingdong Kong, Jing Zhang |
| 2024 | Is Your LiDAR Placement Optimized for 3D Scene Understanding? Ye Li, Lingdong Kong, Hanjiang Hu, Xiaohao Xu, Xiaonan Huang |
| 2024 | Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization. Wei Liu, Zhiying Deng, Zhongyu Niu, Jun Wang, Haozhao Wang, YuanKai Zhang, Ruixuan Li |
| 2024 | Iteration Head: A Mechanistic Study of Chain-of-Thought. Vivien Cabannes, Charles Arnal, Wassim Bouaziz, Xingyu Yang, François Charton, Julia Kempe |
| 2024 | Iterative Methods via Locally Evolving Set Process. Baojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh, Xingzhi Guo, Deqing Yang, Yanghua Xiao |
| 2024 | Iterative Reasoning Preference Optimization. Richard Yuanzhe Pang, Weizhe Yuan, He He, Kyunghyun Cho, Sainbayar Sukhbaatar, Jason Weston |
| 2024 | Iteratively Refined Behavior Regularization for Offline Reinforcement Learning. Yi Ma, Jianye Hao, Xiaohan Hu, Yan Zheng, Chenjun Xiao |
| 2024 | Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval. Ashwin Ramachandran, Vaibhav Raj, Indradyumna Roy, Soumen Chakrabarti, Abir De |
| 2024 | JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong |
| 2024 | Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characters. Haibo Jin, Andy Zhou, Joe D. Menke, Haohan Wang |
| 2024 | JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Garðar Ingvarsson, Timon Willi, Ravi Hammond, Akbir Khan, Christian Schröder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert T. Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktäschel, Chris Lu, Jakob N. Foerster |
| 2024 | JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models. Kun Zhou, Beichen Zhang, Jiapeng Wang, Zhipeng Chen, Xin Zhao, Jing Sha, Zhichao Sheng, Shijin Wang, Ji-Rong Wen |
| 2024 | Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking. Xi Chen, Chuan Qin, Chuyu Fang, Chao Wang, Chen Zhu, Fuzhen Zhuang, Hengshu Zhu, Hui Xiong |
| 2024 | John Ellipsoids via Lazy Updates. David P. Woodruff, Taisuke Yasuda |
| 2024 | Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning. Divyam Madaan, Taro Makino, Sumit Chopra, Kyunghyun Cho |
| 2024 | JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images. Zhecan Wang, Junzhang Liu, Chia-Wei Tang, Hani Alomari, Anushka Sivakumar, Rui Sun, Wenhao Li, Md. Atabuzzaman, Hammad A. Ayyubi, Haoxuan You, Alvi Md. Ishmam, Kai-Wei Chang, Shih-Fu Chang, Christopher Thomas |
| 2024 | Just Add $100 More: Augmenting Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem. Mincheol Chang, Siyeong Lee, Jinkyu Kim, Namil Kim |
| 2024 | KALM: Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts. Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li, Jiaji Zhang, Xiong-Hui Chen, Nan Tang, Yang Yu |
| 2024 | KFNN: K-Free Nearest Neighbor For Crowdsourcing. Wenjun Zhang, Liangxiao Jiang, Chaoqun Li |
| 2024 | KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge. Pengcheng Jiang, Lang Cao, Cao (Danica) Xiao, Parminder Bhatia, Jimeng Sun, Jiawei Han |
| 2024 | KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis. Youngwan Lee, Kwanyong Park, Yoorhim Cho, Yong-Ju Lee, Sung Ju Hwang |
| 2024 | KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization. Tianyi Zhang, Jonah Yi, Zhaozhuo Xu, Anshumali Shrivastava |
| 2024 | KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization. Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami |
| 2024 | Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling. Jiatao Gu, Ying Shen, Shuangfei Zhai, Yizhe Zhang, Navdeep Jaitly, Joshua M. Susskind |
| 2024 | Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning. Xinran Li, Ling Pan, Jun Zhang |
| 2024 | Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting. Fangcheng Liu, Yehui Tang, Zhenhua Liu, Yunsheng Ni, Duyu Tang, Kai Han, Yunhe Wang |
| 2024 | Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, Sanjeev Arora |
| 2024 | Kermut: Composite kernel regression for protein variant effects. Peter Mørch Groth, Mads Herbert Kerrn, Lars Olsen, Jesper Salomon, Wouter Boomsma |
| 2024 | Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen |
| 2024 | Kernel PCA for Out-of-Distribution Detection. Kun Fang, Qinghua Tao, Kexin Lv, Mingzhen He, Xiaolin Huang, Jie Yang |
| 2024 | Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm. Sattar Vakili, Julia Olkhovskaya |
| 2024 | Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features. Chengkai Hou, Zhengrong Xue, Bingyang Zhou, Jinghan Ke, Lin Shao, Huazhe Xu |
| 2024 | KnowGPT: Knowledge Graph based Prompting for Large Language Models. Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha, Zailiang Yu, Xiao Huang |
| 2024 | Knowledge Circuits in Pretrained Transformers. Yunzhi Yao, Ningyu Zhang, Zekun Xi, Mengru Wang, Ziwen Xu, Shumin Deng, Huajun Chen |
| 2024 | Knowledge Composition using Task Vectors with Learned Anisotropic Scaling. Frederic Z. Zhang, Paul Albert, Cristian Rodriguez Opazo, Anton van den Hengel, Ehsan Abbasnejad |
| 2024 | Knowledge Graph Completion by Intermediate Variables Regularization. Changyi Xiao, Yixin Cao |
| 2024 | Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series. Yicheng Luo, Zhen Liu, Linghao Wang, Binquan Wu, Junhao Zheng, Qianli Ma |
| 2024 | KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension. Jie Yang, Wang Zeng, Sheng Jin, Lumin Xu, Wentao Liu, Chen Qian, Ruimao Zhang |
| 2024 | Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference. Rohan Baskar Prabhakar, Hengrui Zhang, David Wentzlaff |
| 2024 | Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks. Felix Dangel, Johannes Müller, Marius Zeinhofer |
| 2024 | Kuro Siwo: 33 billion m Nikolaos-Ioannis Bountos, Maria Sdraka, Angelos Zavras, Andreas Karavias, Ilektra Karasante, Themistocles Herekakis, Angeliki Thanasou, Dimitrios Michail, Ioannis Papoutsis |
| 2024 | L-TTA: Lightweight Test-Time Adaptation Using a Versatile Stem Layer. Jin Shin, Hyun Kim |
| 2024 | L4GM: Large 4D Gaussian Reconstruction Model. Jiawei Ren, Cheng Xie, Ashkan Mirzaei, Hanxue Liang, Xiaohui Zeng, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung Wook Kim, Huan Ling |
| 2024 | LACIE: Listener-Aware Finetuning for Calibration in Large Language Models. Elias Stengel-Eskin, Peter Hase, Mohit Bansal |
| 2024 | LAM3D: Large Image-Point Clouds Alignment Model for 3D Reconstruction from Single Image. Ruikai Cui, Xibin Song, Weixuan Sun, Senbo Wang, Weizhe Liu, Shenzhou Chen, Taizhang Shang, Yang Li, Nick Barnes, Hongdong Li, Pan Ji |
| 2024 | LAVIB: A Large-scale Video Interpolation Benchmark. Alex Stergiou |
| 2024 | LCGen: Mining in Low-Certainty Generation for View-consistent Text-to-3D. Zeng Tao, Tong Yang, Junxiong Lin, Xinji Mai, Haoran Wang, Beining Wang, Enyu Zhou, Yan Wang, Wenqiang Zhang |
| 2024 | LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling. Yaohua Zha, Naiqi Li, Yanzi Wang, Tao Dai, Hang Guo, Bin Chen, Zhi Wang, Zhihao Ouyang, Shu-Tao Xia |
| 2024 | LESS: Label-Efficient and Single-Stage Referring 3D Segmentation. Xuexun Liu, Xiaoxu Xu, Jinlong Li, Qiudan Zhang, Xu Wang, Nicu Sebe, Lin Ma |
| 2024 | LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization. Liang Chen, Yong Zhang, Yibing Song, Zhiqiang Shen, Lingqiao Liu |
| 2024 | LG-CAV: Train Any Concept Activation Vector with Language Guidance. Qihan Huang, Jie Song, Mengqi Xue, Haofei Zhang, Bingde Hu, Huiqiong Wang, Hao Jiang, Xingen Wang, Mingli Song |
| 2024 | LG-VQ: Language-Guided Codebook Learning. Guotao Liang, Baoquan Zhang, Yaowei Wang, Yunming Ye, Xutao Li, Huaibin Wang, Chuyao Luo, Kola Ye, Linfeng Luo |
| 2024 | LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low Resource and Extinct Languages. Andrew M. Bean, Simi Hellsten, Harry Mayne, Jabez Magomere, Ethan Chi, Ryan Chi, Scott Hale, Hannah Rose Kirk |
| 2024 | LION: Linear Group RNN for 3D Object Detection in Point Clouds. Zhe Liu, Jinghua Hou, Xinyu Wang, Xiaoqing Ye, Jingdong Wang, Hengshuang Zhao, Xiang Bai |
| 2024 | LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning. Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi, Jipeng Zhang, Chi Han, Tong Zhang |
| 2024 | LIVE: Learnable In-Context Vector for Visual Question Answering. Yingzhe Peng, Chenduo Hao, Xinting Hu, Jiawei Peng, Xin Geng, Xu Yang |
| 2024 | LLM Circuit Analyses Are Consistent Across Training and Scale. Curt Tigges, Michael Hanna, Qinan Yu, Stella Biderman |
| 2024 | LLM Dataset Inference: Did you train on my dataset? Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic |
| 2024 | LLM Evaluators Recognize and Favor Their Own Generations. Arjun Panickssery, Samuel R. Bowman, Shi Feng |
| 2024 | LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Kristjanson Duvenaud |
| 2024 | LLM-AutoDA: Large Language Model-Driven Automatic Data Augmentation for Long-tailed Problems. Pengkun Wang, Zhe Zhao, Haibin Wen, Fanfu Wang, Binwu Wang, Qingfu Zhang, Yang Wang |
| 2024 | LLM-Check: Investigating Detection of Hallucinations in Large Language Models. Gaurang Sriramanan, Siddhant Bharti, Vinu Sankar Sadasivan, Shoumik Saha, Priyatham Kattakinda, Soheil Feizi |
| 2024 | LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation. Qidong Liu, Xian Wu, Yejing Wang, Zijian Zhang, Feng Tian, Yefeng Zheng, Xiangyu Zhao |
| 2024 | LLM-based Skill Diffusion for Zero-shot Policy Adaptation. Woo Kyung Kim, Youngseok Lee, Jooyoung Kim, Honguk Woo |
| 2024 | LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment. Ge Yang, ChangYi He, Jinyang Guo, Jianyu Wu, Yifu Ding, Aishan Liu, Haotong Qin, Pengliang Ji, Xianglong Liu |
| 2024 | LLMDFA: Analyzing Dataflow in Code with Large Language Models. Chengpeng Wang, Wuqi Zhang, Zian Su, Xiangzhe Xu, Xiaoheng Xie, Xiangyu Zhang |
| 2024 | LLMs Can Evolve Continually on Modality for X-Modal Reasoning. Jiazuo Yu, Haomiao Xiong, Lu Zhang, Haiwen Diao, Yunzhi Zhuge, Lanqing Hong, Dong Wang, Huchuan Lu, You He, Long Chen |
| 2024 | LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings. Duo Wang, Yuan Zuo, Fengzhi Li, Junjie Wu |
| 2024 | LLaMo: Large Language Model-based Molecular Graph Assistant. Jinyoung Park, Minseong Bae, Dohwan Ko, Hyunwoo J. Kim |
| 2024 | LLaNA: Large Language and NeRF Assistant. Andrea Amaduzzi, Pierluigi Zama Ramirez, Giuseppe Lisanti, Samuele Salti, Luigi Di Stefano |
| 2024 | LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model. Zecheng Hao, Xinyu Shi, Yujia Liu, Zhaofei Yu, Tiejun Huang |
| 2024 | LOVA3: Learning to Visual Question Answering, Asking and Assessment. Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Zechen Bai, Mike Zheng Shou |
| 2024 | LP-3DGS: Learning to Prune 3D Gaussian Splatting. Zhaoliang Zhang, Tianchen Song, Yongjae Lee, Li Yang, Cheng Peng, Rama Chellappa, Deliang Fan |
| 2024 | LRM-Zero: Training Large Reconstruction Models with Synthesized Data. Desai Xie, Sai Bi, Zhixin Shu, Kai Zhang, Zexiang Xu, Yi Zhou, Sören Pirk, Arie E. Kaufman, Xin Sun, Hao Tan |
| 2024 | LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing. Xiaonan Nie, Qibin Liu, Fangcheng Fu, Shenhan Zhu, Xupeng Miao, Xiaoyang Li, Yang Zhang, Shouda Liu, Bin Cui |
| 2024 | LT-Defense: Searching-free Backdoor Defense via Exploiting the Long-tailed Effect. Yixiao Xu, Binxing Fang, Mohan Li, Keke Tang, Zhihong Tian |
| 2024 | LVD-2M: A Long-take Video Dataset with Temporally Dense Captions. Tianwei Xiong, Yuqing Wang, Daquan Zhou, Zhijie Lin, Jiashi Feng, Xihui Liu |
| 2024 | LaKD: Length-agnostic Knowledge Distillation for Trajectory Prediction with Any Length Observations. Yuhang Li, Changsheng Li, Ruilin Lv, Rongqing Li, Ye Yuan, Guoren Wang |
| 2024 | LaSCal: Label-Shift Calibration without target labels. Teodora Popordanoska, Gorjan Radevski, Tinne Tuytelaars, Matthew B. Blaschko |
| 2024 | LaSe-E2V: Towards Language-guided Semantic-aware Event-to-Video Reconstruction. Kanghao Chen, Hangyu Li, Jiazhou Zhou, Zeyu Wang, Lin Wang |
| 2024 | Label Delay in Online Continual Learning. Botos Csaba, Wenxuan Zhang, Matthias Müller, Ser Nam Lim, Philip Torr, Adel Bibi |
| 2024 | Label Noise: Ignorance Is Bliss. Yilun Zhu, Jianxin Zhang, Aditya Gangrade, Clayton Scott |
| 2024 | Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases. Hang Yin, Liyao Xiang, Dong Ding, Yuheng He, Yihan Wu, Pengzhi Chu, Xinbing Wang, Chenghu Zhou |
| 2024 | Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning. Eli Chien, Haoyu Wang, Ziang Chen, Pan Li |
| 2024 | Language Generation in the Limit. Jon M. Kleinberg, Sendhil Mullainathan |
| 2024 | Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication. Huao Li, Hossein Nourkhiz Mahjoub, Behdad Chalaki, Vaishnav Tadiparthi, Kwonjoon Lee, Ehsan Moradi-Pari, Charles Lewis, Katia P. Sycara |
| 2024 | Language Model as Visual Explainer. Xingyi Yang, Xinchao Wang |
| 2024 | Language Models as Hierarchy Encoders. Yuan He, Moy Yuan, Jiaoyan Chen, Ian Horrocks |
| 2024 | Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models. Hui-Po Wang, Mario Fritz |
| 2024 | Language Without Borders: A Dataset and Benchmark for Code-Switching Lip Reading. Xueyi Zhang, Mingrui Lao, Peng Zhao, Jun Tang, Yanming Guo, Siqi Cai, Xianghu Yue, Haizhou Li |
| 2024 | Language-Driven Interactive Traffic Trajectory Generation. Junkai Xia, Chenxin Xu, Qingyao Xu, Yanfeng Wang, Siheng Chen |
| 2024 | Large Language Model Unlearning via Embedding-Corrupted Prompts. Chris Yuhao Liu, Yaxuan Wang, Jeffrey Flanigan, Yang Liu |
| 2024 | Large Language Model Unlearning. Yuanshun Yao, Xiaojun Xu, Yang Liu |
| 2024 | Large Language Models Must Be Taught to Know What They Don't Know. Sanyam Kapoor, Nate Gruver, Manley Roberts, Katie Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson |
| 2024 | Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach. Weiyu Ma, Qirui Mi, Yongcheng Zeng, Xue Yan, Runji Lin, Yuqiao Wu, Jun Wang, Haifeng Zhang |
| 2024 | Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation. Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu, Makoto Onizuka, Ryosuke Shibasaki, Noboru Koshizuka, Chuan Xiao |
| 2024 | Large Language Models' Expert-level Global History Knowledge Benchmark (HiST-LLM). Jakob Hauser, Dániel Kondor, Jenny Reddish, Majid Benam, Enrico Cioni, Federica Villa, James Bennett, Daniel Hoyer, Pieter Francois, Peter Turchin, R. Maria del Rio Chanona |
| 2024 | Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning. Jiapu Wang, Kai Sun, Linhao Luo, Wei Wei, Yongli Hu, Alan Wee-Chung Liew, Shirui Pan, Baocai Yin |
| 2024 | Large Pre-trained time series models for cross-domain Time series analysis tasks. Harshavardhan Kamarthi, B. Aditya Prakash |
| 2024 | Large Scale Transfer Learning for Tabular Data via Language Modeling. Josh Gardner, Juan C. Perdomo, Ludwig Schmidt |
| 2024 | Large Spatial Model: End-to-end Unposed Images to Semantic 3D. Zhiwen Fan, Jian Zhang, Wenyan Cong, Peihao Wang, Renjie Li, Kairun Wen, Shijie Zhou, Achuta Kadambi, Zhangyang Wang, Danfei Xu, Boris Ivanovic, Marco Pavone |
| 2024 | Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett |
| 2024 | Large language model validity via enhanced conformal prediction methods. John J. Cherian, Isaac Gibbs, Emmanuel J. Candès |
| 2024 | Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities. Zaiwei Chen, Eric Mazumdar |
| 2024 | Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning. Alessandro Montenegro, Marco Mussi, Matteo Papini, Alberto Maria Metelli |
| 2024 | Latent Diffusion for Neural Spiking Data. Jaivardhan Kapoor, Auguste Schulz, Julius Vetter, Felix Pei, Richard Gao, Jakob H. Macke |
| 2024 | Latent Functional Maps: a spectral framework for representation alignment. Marco Fumero, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, Emanuele Rodolà |
| 2024 | Latent Intrinsics Emerge from Training to Relight. Xiao Zhang, William Gao, Seemandhar Jain, Michael Maire, David A. Forsyth, Anand Bhattad |
| 2024 | Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning. Gaia Molinaro, Cédric Colas, Pierre-Yves Oudeyer, Anne Collins |
| 2024 | Latent Neural Operator for Solving Forward and Inverse PDE Problems. Tian Wang, Chuang Wang |
| 2024 | Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models. Minki Kang, Sung Ju Hwang, Gibbeum Lee, Jaewoong Cho |
| 2024 | Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference. Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu |
| 2024 | Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks. Victor Boutin, Rishav Mukherji, Aditya Agrawal, Sabine Muzellec, Thomas Fel, Thomas Serre, Rufin VanRullen |
| 2024 | Layer-Adaptive State Pruning for Deep State Space Models. Minseon Gwak, Seongrok Moon, Joohwan Ko, PooGyeon Park |
| 2024 | LeDex: Training LLMs to Better Self-Debug and Explain Code. Nan Jiang, Xiaopeng Li, Shiqi Wang, Qiang Zhou, Soneya Binta Hossain, Baishakhi Ray, Varun Kumar, Xiaofei Ma, Anoop Deoras |
| 2024 | Lean Workbook: A large-scale Lean problem set formalized from natural language math problems. Huaiyuan Ying, Zijian Wu, Yihan Geng, Jiayu Wang, Dahua Lin, Kai Chen |
| 2024 | Learn To be Efficient: Build Structured Sparsity in Large Language Models. Haizhong Zheng, Xiaoyan Bai, Xueshen Liu, Zhuoqing Morley Mao, Beidi Chen, Fan Lai, Atul Prakash |
| 2024 | Learn more, but bother less: parameter efficient continual learning. Fuli Qiao, Mehrdad Mahdavi |
| 2024 | Learnability Matters: Active Learning for Video Captioning. Yiqian Zhang, Buyu Liu, Jun Bao, Qiang Huang, Min Zhang, Jun Yu |
| 2024 | Learnability of high-dimensional targets by two-parameter models and gradient flow. Dmitry Yarotsky |
| 2024 | Learning 1D Causal Visual Representation with De-focus Attention Networks. Chenxin Tao, Xizhou Zhu, Shiqian Su, Lewei Lu, Changyao Tian, Xuan Luo, Gao Huang, Hongsheng Li, Yu Qiao, Jie Zhou, Jifeng Dai |
| 2024 | Learning 3D Equivariant Implicit Function with Patch-Level Pose-Invariant Representation. Xin Hu, Xiaole Tang, Ruixuan Yu, Jian Sun |
| 2024 | Learning 3D Garment Animation from Trajectories of A Piece of Cloth. Yidi Shao, Chen Change Loy, Bo Dai |
| 2024 | Learning Action and Reasoning-Centric Image Editing from Videos and Simulation. Benno Krojer, Dheeraj Vattikonda, Luis Lara, Varun Jampani, Eva Portelance, Chris Pal, Siva Reddy |
| 2024 | Learning Better Representations From Less Data For Propositional Satisfiability. Mohamed Ghanem, Frederik Schmitt, Julian Siber, Bernd Finkbeiner |
| 2024 | Learning Bregman Divergences with Application to Robustness. Mohamed-Hicham Leghettas, Markus Püschel |
| 2024 | Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification. Jiangming Shi, Xiangbo Yin, Yachao Zhang, Zhizhong Zhang, Yuan Xie, Yanyun Qu |
| 2024 | Learning Complete Protein Representation by Dynamically Coupling of Sequence and Structure. Bozhen Hu, Cheng Tan, Jun Xia, Yue Liu, Lirong Wu, Jiangbin Zheng, Yongjie Xu, Yufei Huang, Stan Z. Li |
| 2024 | Learning Cooperative Trajectory Representations for Motion Forecasting. Hongzhi Ruan, Haibao Yu, Wenxian Yang, Siqi Fan, Zaiqing Nie |
| 2024 | Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios. Shihan Ma, Bo Hu, Tianyu Jia, Alexander Kenneth Clarke, Blanka Zicher, Arnault H. Caillet, Dario Farina, José C. Príncipe |
| 2024 | Learning Cut Generating Functions for Integer Programming. Hongyu Cheng, Amitabh Basu |
| 2024 | Learning De-Biased Representations for Remote-Sensing Imagery. Zichen Tian, Zhaozheng Chen, Qianru Sun |
| 2024 | Learning Diffusion Priors from Observations by Expectation Maximization. François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe |
| 2024 | Learning Discrete Concepts in Latent Hierarchical Models. Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang |
| 2024 | Learning Discrete Latent Variable Structures with Tensor Rank Conditions. Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang |
| 2024 | Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization. Ziyu Shan, Yujie Zhang, Yipeng Liu, Yiling Xu |
| 2024 | Learning Distinguishable Trajectory Representation with Contrastive Loss. Tianxu Li, Kun Zhu, Juan Li, Yang Zhang |
| 2024 | Learning Distributions on Manifolds with Free-Form Flows. Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Ullrich Köthe |
| 2024 | Learning Elastic Costs to Shape Monge Displacements. Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi |
| 2024 | Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem. Fivos Kalogiannis, Jingming Yan, Ioannis Panageas |
| 2024 | Learning Formal Mathematics From Intrinsic Motivation. Gabriel Poesia, David Broman, Nick Haber, Noah D. Goodman |
| 2024 | Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation. Qi Bi, Jingjun Yi, Hao Zheng, Haolan Zhan, Yawen Huang, Wei Ji, Yuexiang Li, Yefeng Zheng |
| 2024 | Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm. Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal |
| 2024 | Learning Generalized Linear Programming Value Functions. Tu Anh-Nguyen, Joey Huchette, Christian Tjandraatmadja |
| 2024 | Learning Goal-Conditioned Representations for Language Reward Models. Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx |
| 2024 | Learning Group Actions on Latent Representations. Yinzhu Jin, Aman Shrivastava, Tom Fletcher |
| 2024 | Learning Human-like Representations to Enable Learning Human Values. Andrea Wynn, Ilia Sucholutsky, Tom Griffiths |
| 2024 | Learning Identifiable Factorized Causal Representations of Cellular Responses. Haiyi Mao, Romain Lopez, Kai Liu, Jan-Christian Huetter, David Richmond, Panayiotis V. Benos, Lin Qiu |
| 2024 | Learning Image Priors Through Patch-Based Diffusion Models for Solving Inverse Problems. Jason Hu, Bowen Song, Xiaojian Xu, Liyue Shen, Jeffrey A. Fessler |
| 2024 | Learning Infinitesimal Generators of Continuous Symmetries from Data. Gyeonghoon Ko, Hyunsu Kim, Juho Lee |
| 2024 | Learning Interaction-aware 3D Gaussian Splatting for One-shot Hand Avatars. Xuan Huang, Hanhui Li, Wanquan Liu, Xiaodan Liang, Yiqiang Yan, Yuhao Cheng, Chenqiang Gao |
| 2024 | Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity. Jikai Jin, Vasilis Syrgkanis |
| 2024 | Learning Low-Rank Feature for Thorax Disease Classification. Yancheng Wang, Rajeev Goel, Utkarsh Nath, Alvin C. Silva, Teresa Wu, Yingzhen Yang |
| 2024 | Learning Macroscopic Dynamics from Partial Microscopic Observations. Mengyi Chen, Qianxiao Li |
| 2024 | Learning Mixtures of Unknown Causal Interventions. Abhinav Kumar, Kirankumar Shiragur, Caroline Uhler |
| 2024 | Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient. Steven Li, Rickmer Krohn, Tao Chen, Anurag Ajay, Pulkit Agrawal, Georgia Chalvatzaki |
| 2024 | Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees. Sean Jaffe, Alexander Davydov, Deniz Lapsekili, Ambuj K. Singh, Francesco Bullo |
| 2024 | Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random. Gautam Chandrasekaran, Vasilis Kontonis, Konstantinos Stavropoulos, Kevin Tian |
| 2024 | Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression. Xi Zhang, Xiaolin Wu |
| 2024 | Learning Optimal Tax Design in Nonatomic Congestion Games. Qiwen Cui, Maryam Fazel, Simon S. Du |
| 2024 | Learning Partitions from Context. Simon Buchholz |
| 2024 | Learning Place Cell Representations and Context-Dependent Remapping. Markus Pettersen, Frederik Rogge, Mikkel E. Lepperød |
| 2024 | Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation. Xinhao Zheng, Yang Li, Cunxin Fan, Huaijin Wu, Xinhao Song, Junchi Yan |
| 2024 | Learning Representations for Hierarchies with Minimal Support. Benjamin Rozonoyer, Michael Boratko, Dhruvesh Patel, Wenlong Zhao, Shib Sankar Dasgupta, Hung Le, Andrew McCallum |
| 2024 | Learning Segmentation from Point Trajectories. Laurynas Karazija, Iro Laina, Christian Rupprecht, Andrea Vedaldi |
| 2024 | Learning Social Welfare Functions. Kanad Pardeshi, Itai Shapira, Ariel D. Procaccia, Aarti Singh |
| 2024 | Learning Spatially-Aware Language and Audio Embeddings. Bhavika Devnani, Skyler Seto, Zakaria Aldeneh, Alessandro Toso, Elena Menyaylenko, Barry-John Theobald, Jonathan Sheaffer, Miguel Sarabia |
| 2024 | Learning Structure-Aware Representations of Dependent Types. Konstantinos Kogkalidis, Orestis Melkonian, Jean-Philippe Bernardy |
| 2024 | Learning Structured Representations with Hyperbolic Embeddings. Aditya Sinha, Siqi Zeng, Makoto Yamada, Han Zhao |
| 2024 | Learning Successor Features the Simple Way. Raymond Chua, Arna Ghosh, Christos Kaplanis, Blake A. Richards, Doina Precup |
| 2024 | Learning Superconductivity from Ordered and Disordered Material Structures. Pin Chen, Luoxuan Peng, Rui Jiao, Qing Mo, Zhen Wang, Wenbing Huang, Yang Liu, Yutong Lu |
| 2024 | Learning Transferable Features for Implicit Neural Representations. Kushal Kardam Vyas, Ahmed Imtiaz Humayun, Aniket Dashpute, Richard G. Baraniuk, Ashok Veeraraghavan, Guha Balakrishnan |
| 2024 | Learning Truncated Causal History Model for Video Restoration. Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh, Di Niu |
| 2024 | Learning Versatile Skills with Curriculum Masking. Yao Tang, Zhihui Xie, Zichuan Lin, Deheng Ye, Shuai Li |
| 2024 | Learning Where to Edit Vision Transformers. Yunqiao Yang, Long-Kai Huang, Shengzhuang Chen, Kede Ma, Ying Wei |
| 2024 | Learning World Models for Unconstrained Goal Navigation. Yuanlin Duan, Wensen Mao, He Zhu |
| 2024 | Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise. Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas, Jelena Diakonikolas |
| 2024 | Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training. Haoran He, Chenjia Bai, Ling Pan, Weinan Zhang, Bin Zhao, Xuelong Li |
| 2024 | Learning and Transferring Sparse Contextual Bigrams with Linear Transformers. Yunwei Ren, Zixuan Wang, Jason D. Lee |
| 2024 | Learning diffusion at lightspeed. Antonio Terpin, Nicolas Lanzetti, Martín Gadea, Florian Dörfler |
| 2024 | Learning diverse causally emergent representations from time series data. David McSharry, Christos Kaplanis, Fernando Rosas, Pedro A. M. Mediano |
| 2024 | Learning from Highly Sparse Spatio-temporal Data. Leyan Deng, Chenwang Wu, Defu Lian, Enhong Chen |
| 2024 | Learning from Noisy Labels via Conditional Distributionally Robust Optimization. Hui Guo, Grace Yi, Boyu Wang |
| 2024 | Learning from Offline Foundation Features with Tensor Augmentations. Emir Konuk, Christos Matsoukas, Moein Sorkhei, Phitchapha Lertsiravarameth, Kevin Smith |
| 2024 | Learning from Pattern Completion: Self-supervised Controllable Generation. Zhiqiang Chen, Guofan Fan, Jinying Gao, Lei Ma, Bo Lei, Tiejun Huang, Shan Yu |
| 2024 | Learning from Snapshots of Discrete and Continuous Data Streams. Pramith Devulapalli, Steve Hanneke |
| 2024 | Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate. Can Jin, Tong Che, Hongwu Peng, Yiyuan Li, Dimitris N. Metaxas, Marco Pavone |
| 2024 | Learning from Uncertain Data: From Possible Worlds to Possible Models. Jiongli Zhu, Su Feng, Boris Glavic, Babak Salimi |
| 2024 | Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks. Eszter Székely, Lorenzo Bardone, Federica Gerace, Sebastian Goldt |
| 2024 | Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms. Thanh Nguyen-Tang, Raman Arora |
| 2024 | Learning on Large Graphs using Intersecting Communities. Ben Finkelshtein, Ismail Ilkan Ceylan, Michael M. Bronstein, Ron Levie |
| 2024 | Learning predictable and robust neural representations by straightening image sequences. Xueyan Niu, Cristina Savin, Eero P. Simoncelli |
| 2024 | Learning rigid-body simulators over implicit shapes for large-scale scenes and vision. Yulia Rubanova, Tatiana Lopez-Guevara, Kelsey R. Allen, Will Whitney, Kimberly L. Stachenfeld, Tobias Pfaff |
| 2024 | Learning symmetries via weight-sharing with doubly stochastic tensors. Putri A. van der Linden, Alejandro García-Castellanos, Sharvaree P. Vadgama, Thijs P. Kuipers, Erik J. Bekkers |
| 2024 | Learning the Expected Core of Strictly Convex Stochastic Cooperative Games. Nam Phuong Tran, The-Anh Ta, Shuqing Shi, Debmalya Mandal, Yali Du, Long Tran-Thanh |
| 2024 | Learning the Infinitesimal Generator of Stochastic Diffusion Processes. Vladimir Kostic, Hélène Halconruy, Timothée Devergne, Karim Lounici, Massimiliano Pontil |
| 2024 | Learning the Latent Causal Structure for Modeling Label Noise. Yexiong Lin, Yu Yao, Tongliang Liu |
| 2024 | Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards. Qinwei Yang, Xueqing Liu, Yan Zeng, Ruocheng Guo, Yang Liu, Peng Wu |
| 2024 | Learning to Assist Humans without Inferring Rewards. Vivek Myers, Evan Ellis, Sergey Levine, Benjamin Eysenbach, Anca D. Dragan |
| 2024 | Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games. Fanqi Kong, Yizhe Huang, Song-Chun Zhu, Siyuan Qi, Xue Feng |
| 2024 | Learning to Cooperate with Humans using Generative Agents. Yancheng Liang, Daphne Chen, Abhishek Gupta, Simon S. Du, Natasha Jaques |
| 2024 | Learning to Decouple the Lights for 3D Face Texture Modeling. Tianxin Huang, Zhenyu Zhang, Ying Tai, Gim Hee Lee |
| 2024 | Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf. Xuanfa Jin, Ziyan Wang, Yali Du, Meng Fang, Haifeng Zhang, Jun Wang |
| 2024 | Learning to Edit Visual Programs with Self-Supervision. R. Kenny Jones, Renhao Zhang, Aditya Ganeshan, Daniel Ritchie |
| 2024 | Learning to Embed Distributions via Maximum Kernel Entropy. Oleksii Kachaiev, Stefano Recanatesi |
| 2024 | Learning to Handle Complex Constraints for Vehicle Routing Problems. Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang |
| 2024 | Learning to Merge Tokens via Decoupled Embedding for Efficient Vision Transformers. Dong Hoon Lee, Seunghoon Hong |
| 2024 | Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality. Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus |
| 2024 | Learning to Predict Structural Vibrations. Jan van Delden, Julius Schultz, Christopher Blech, Sabine C. Langer, Timo Lüddecke |
| 2024 | Learning to Price Homogeneous Data. Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy |
| 2024 | Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios. Shantanu Jaiswal, Debaditya Roy, Basura Fernando, Cheston Tan |
| 2024 | Learning to Reason via Program Generation, Emulation, and Search. Nathaniel Weir, Muhammad Khalifa, Linlu Qiu, Orion Weller, Peter Clark |
| 2024 | Learning to Shape In-distribution Feature Space for Out-of-distribution Detection. Yonggang Zhang, Jie Lu, Bo Peng, Zhen Fang, Yiu-ming Cheung |
| 2024 | Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way. Ming Chen, Jie Chun, Shang Xiang, Luona Wei, Yonghao Du, Qian Wan, Yuning Chen, Yingwu Chen |
| 2024 | Learning to Understand: Identifying Interactions via the Möbius Transform. Justin Singh Kang, Yigit Efe Erginbas, Landon Butler, Ramtin Pedarsani, Kannan Ramchandran |
| 2024 | Learning to be Smooth: An End-to-End Differentiable Particle Smoother. Ali Younis, Erik B. Sudderth |
| 2024 | Learning to compute Gröbner bases. Hiroshi Kera, Yuki Ishihara, Yuta Kambe, Tristan Vaccon, Kazuhiro Yokoyama |
| 2024 | Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks. Tianyu He, Darshil Doshi, Aritra Das, Andrey Gromov |
| 2024 | Learning via Surrogate PAC-Bayes. Antoine Picard-Weibel, Roman Moscoviz, Benjamin Guedj |
| 2024 | Learning with Fitzpatrick Losses. Seta Rakotomandimby, Jean-Philippe Chancelier, Michel De Lara, Mathieu Blondel |
| 2024 | Learning-Augmented Algorithms for the Bahncard Problem. Hailiang Zhao, Xueyan Tang, Peng Chen, Shuiguang Deng |
| 2024 | Learning-Augmented Algorithms with Explicit Predictors. Marek Eliás, Haim Kaplan, Yishay Mansour, Shay Moran |
| 2024 | Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems. Vincent Cohen-Addad, Tommaso d'Orsi, Anupam Gupta, Euiwoong Lee, Debmalya Panigrahi |
| 2024 | Learning-Augmented Dynamic Submodular Maximization. Arpit Agarwal, Eric Balkanski |
| 2024 | Learning-Augmented Priority Queues. Ziyad Benomar, Christian Coester |
| 2024 | Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching. Xinyin Ma, Gongfan Fang, Michael Bi Mi, Xinchao Wang |
| 2024 | Least Squares Regression Can Exhibit Under-Parameterized Double Descent. Xinyue Li, Rishi Sonthalia |
| 2024 | Length Optimization in Conformal Prediction. Shayan Kiyani, George J. Pappas, Hamed Hassani |
| 2024 | Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models. Xu Yang, Yingzhe Peng, Haoxuan Ma, Shuo Xu, Chi Zhang, Yucheng Han, Hanwang Zhang |
| 2024 | Leveraging Catastrophic Forgetting to Develop Safe Diffusion Models against Malicious Finetuning. Jiadong Pan, Hongcheng Gao, Zongyu Wu, Taihang Hu, Li Su, Qingming Huang, Liang Li |
| 2024 | Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers. Jinsong Chen, Hanpeng Liu, John E. Hopcroft, Kun He |
| 2024 | Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models. Ruofeng Yang, Zhijie Wang, Bo Jiang, Shuai Li |
| 2024 | Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models. Sadegh Mahdavi, Raquel Aoki, Keyi Tang, Yanshuai Cao |
| 2024 | Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation. Jian Hu, Jiayi Lin, Junchi Yan, Shaogang Gong |
| 2024 | Leveraging Separated World Model for Exploration in Visually Distracted Environments. Kaichen Huang, Shenghua Wan, Minghao Shao, Hai-Hang Sun, Le Gan, Shuai Feng, De-Chuan Zhan |
| 2024 | Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images. Junxian Wu, Xinyi Ke, Xiaoming Jiang, Huanwen Wu, Youyong Kong, Lizhi Shao |
| 2024 | Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning. Alex Jinpeng Wang, Linjie Li, Yiqi Lin, Min Li, Lijuan Wang, Mike Zheng Shou |
| 2024 | Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals. Lisa Bedin, Gabriel Cardoso, Josselin Duchateau, Rémi Dubois, Eric Moulines |
| 2024 | Leveraging partial stragglers within gradient coding. Aditya Ramamoorthy, Ruoyu Meng, Vrinda S. Girimaji |
| 2024 | LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models. Haitao Li, You Chen, Qingyao Ai, Yueyue Wu, Ruizhe Zhang, Yiqun Liu |
| 2024 | Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding. Yunze Man, Shuhong Zheng, Zhipeng Bao, Martial Hebert, Liangyan Gui, Yu-Xiong Wang |
| 2024 | LiT: Unifying LiDAR "Languages" with LiDAR Translator. Yixing Lao, Tao Tang, Xiaoyang Wu, Peng Chen, Kaicheng Yu, Hengshuang Zhao |
| 2024 | LibAMM: Empirical Insights into Approximate Computing for Accelerating Matrix Multiplication. Xianzhi Zeng, Wenchao Jiang, Shuhao Zhang |
| 2024 | LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch. Xiaoyuan Zhang, Liang Zhao, Yingying Yu, Xi Lin, Yifan Chen, Han Zhao, Qingfu Zhang |
| 2024 | Light Unbalanced Optimal Transport. Milena Gazdieva, Arip Asadulaev, Evgeny Burnaev, Aleksandr Korotin |
| 2024 | LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS. Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, Zhangyang Wang |
| 2024 | Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis. Xin Jin, Pengyi Jiao, Zheng-Peng Duan, Xingchao Yang, Chongyi Li, Chun-Le Guo, Bo Ren |
| 2024 | Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation. Jintao Tong, Yixiong Zou, Yuhua Li, Ruixuan Li |
| 2024 | Limits of Transformer Language Models on Learning to Compose Algorithms. Jonathan Thomm, Giacomo Camposampiero, Aleksandar Terzic, Michael Hersche, Bernhard Schölkopf, Abbas Rahimi |
| 2024 | LinNet: Linear Network for Efficient Point Cloud Representation Learning. Hao Deng, Kunlei Jing, Shengmei Chen, Cheng Liu, Jiawei Ru, Bo Jiang, Lin Wang |
| 2024 | Linear Causal Bandits: Unknown Graph and Soft Interventions. Zirui Yan, Ali Tajer |
| 2024 | Linear Causal Representation Learning from Unknown Multi-node Interventions. Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer |
| 2024 | Linear Regression using Heterogeneous Data Batches. Ayush Jain, Rajat Sen, Weihao Kong, Abhimanyu Das, Alon Orlitsky |
| 2024 | Linear Time Approximation Algorithm for Column Subset Selection with Local Search. Yuanbin Zou, Ziyun Huang, Jinhui Xu, Jianxin Wang, Qilong Feng |
| 2024 | Linear Transformers are Versatile In-Context Learners. Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge |
| 2024 | Linear Uncertainty Quantification of Graphical Model Inference. Chenghua Guo, Han Yu, Jiaxin Liu, Chao Chen, Qi Li, Sihong Xie, Xi Zhang |
| 2024 | Linearly Decomposing and Recomposing Vision Transformers for Diverse-Scale Models. Shuxia Lin, Miaosen Zhang, Ruiming Chen, Xu Yang, Qiufeng Wang, Xin Geng |
| 2024 | Linguistic Collapse: Neural Collapse in (Large) Language Models. Robert Wu, Vardan Papyan |
| 2024 | Linking In-context Learning in Transformers to Human Episodic Memory. Ji-An Li, Corey Yishan Zhou, Marcus K. Benna, Marcelo G. Mattar |
| 2024 | Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes. Weifeng Liu, Tianyi She, Jiawei Liu, Boheng Li, Dongyu Yao, Ziyou Liang, Run Wang |
| 2024 | Lisa: Lazy Safety Alignment for Large Language Models against Harmful Fine-tuning Attack. Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim F. Tekin, Ling Liu |
| 2024 | Listenable Maps for Zero-Shot Audio Classifiers. Francesco Paissan, Luca Della Libera, Mirco Ravanelli, Cem Subakan |
| 2024 | LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models. Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, Romann M. Weber |
| 2024 | LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Control and Rendering. Delin Qu, Qizhi Chen, Pingrui Zhang, Xianqiang Gao, Bin Zhao, Zhigang Wang, Dong Wang, Xuelong Li |
| 2024 | LoCo: Learning 3D Location-Consistent Image Features with a Memory-Efficient Ranking Loss. Dominik A. Kloepfer, João F. Henriques, Dylan Campbell |
| 2024 | LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment. Juelin Zhu, Shen Yan, Long Wang, Shengyue Zhang, Yu Liu, Maojun Zhang |
| 2024 | LoFiT: Localized Fine-tuning on LLM Representations. Fangcong Yin, Xi Ye, Greg Durrett |
| 2024 | LoQT: Low-Rank Adapters for Quantized Pretraining. Sebastian Loeschcke, Mads Toftrup, Michael J. Kastoryano, Serge J. Belongie, Vésteinn Snæbjarnarson |
| 2024 | LoRA-GA: Low-Rank Adaptation with Gradient Approximation. Shaowen Wang, Linxi Yu, Jian Li |
| 2024 | LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search. Elias Jääsaari, Ville Hyvönen, Teemu Roos |
| 2024 | LoTLIP: Improving Language-Image Pre-training for Long Text Understanding. Wei Wu, Kecheng Zheng, Shuailei Ma, Fan Lu, Yuxin Guo, Yifei Zhang, Wei Chen, Qingpei Guo, Yujun Shen, Zheng-Jun Zha |
| 2024 | LocCa: Visual Pretraining with Location-aware Captioners. Bo Wan, Michael Tschannen, Yongqin Xian, Filip Pavetic, Ibrahim M. Alabdulmohsin, Xiao Wang, André Susano Pinto, Andreas Steiner, Lucas Beyer, Xiaohua Zhai |
| 2024 | Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit. Seok-Jin Kim, Min-hwan Oh |
| 2024 | Local Curvature Smoothing with Stein's Identity for Efficient Score Matching. Genki Osada, Makoto Shing, Takashi Nishide |
| 2024 | Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs. Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli |
| 2024 | Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning. Minghui Chen, Meirui Jiang, Xin Zhang, Qi Dou, Zehua Wang, Xiaoxiao Li |
| 2024 | Local and Adaptive Mirror Descents in Extensive-Form Games. Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko |
| 2024 | Local to Global: Learning Dynamics and Effect of Initialization for Transformers. Ashok Vardhan Makkuva, Marco Bondaschi, Adway Girish, Alliot Nagle, Hyeji Kim, Michael Gastpar, Chanakya Ekbote |
| 2024 | Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner. Xing Cui, Peipei Li, Zekun Li, Xuannan Liu, Yueying Zou, Zhaofeng He |
| 2024 | Localized Adaptive Risk Control. Matteo Zecchin, Osvaldo Simeone |
| 2024 | Localized Zeroth-Order Prompt Optimization. Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low |
| 2024 | Localizing Memorization in SSL Vision Encoders. Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch |
| 2024 | Locally Private and Robust Multi-Armed Bandits. Xingyu Zhou, Komo (Wei) Zhang |
| 2024 | Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild. Jianan Yang, Chenchao Gao, Zhiqing Xiao, Junbo Zhao, Sai Wu, Gang Chen, Haobo Wang |
| 2024 | Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk. Yuzhou Gu, Nikki Lijing Kuang, Yian Ma, Zhao Song, Lichen Zhang |
| 2024 | Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning. Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin |
| 2024 | LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation. Bowen Li, Zhaoyu Li, Qiwei Du, Jinqi Luo, Wenshan Wang, Yaqi Xie, Simon Stepputtis, Chen Wang, Katia P. Sycara, Pradeep Ravikumar, Alexander G. Gray, Xujie Si, Sebastian A. Scherer |
| 2024 | Logical characterizations of recurrent graph neural networks with reals and floats. Veeti Ahvonen, Damian Heiman, Antti Kuusisto, Carsten Lutz |
| 2024 | Loki: Low-rank Keys for Efficient Sparse Attention. Prajwal Singhania, Siddharth Singh, Shwai He, Soheil Feizi, Abhinav Bhatele |
| 2024 | Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments. Siddharth Nayak, Adelmo Morrison Orozco, Marina Ten Have, Jackson Zhang, Vittal Thirumalai, Darren Chen, Aditya Kapoor, Eric Robinson, Karthik Gopalakrishnan, James Harrison, Anuj Mahajan, Brian Ichter, Hamsa Balakrishnan |
| 2024 | Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie Stimuli. Liwei Huang, Zhengyu Ma, Liutao Yu, Huihui Zhou, Yonghong Tian |
| 2024 | Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation. Wenjun Miao, Guansong Pang, Jin Zheng, Xiao Bai |
| 2024 | Long-form factuality in large language models. Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le |
| 2024 | Long-range Brain Graph Transformer. Shuo Yu, Shan Jin, Ming Li, Tabinda Sarwar, Feng Xia |
| 2024 | Long-range Meta-path Search on Large-scale Heterogeneous Graphs. Chao Li, Zijie Guo, Qiuting He, Kun He |
| 2024 | Long-tailed Object Detection Pretraining: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction. Chen-Long Duan, Yong Li, Xiu-Shen Wei, Lin Zhao |
| 2024 | LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding. Haoning Wu, Dongxu Li, Bei Chen, Junnan Li |
| 2024 | Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering. Jie Ma, Min Hu, Pinghui Wang, Wangchun Sun, Lingyun Song, Hongbin Pei, Jun Liu, Youtian Du |
| 2024 | LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate. Anthony Fuller, Daniel G. Kyrollos, Yousef Yassin, James R. Green |
| 2024 | Lookback Prophet Inequalities. Ziyad Benomar, Dorian Baudry, Vianney Perchet |
| 2024 | Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models. Regev Cohen, Idan Kligvasser, Ehud Rivlin, Daniel Freedman |
| 2024 | Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics. Jonas Spinner, Victor Bresó, Pim de Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer |
| 2024 | Loss Landscape Characterization of Neural Networks without Over-Parametrization. Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurélien Lucchi |
| 2024 | Low Degree Hardness for Broadcasting on Trees. Han Huang, Elchanan Mossel |
| 2024 | Low Precision Local Training is Enough for Federated Learning. Zhiwei Li, Yiqiu Li, Binbin Lin, Zhongming Jin, Weizhong Zhang |
| 2024 | Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling. Peter Halmos, Xinhao Liu, Julian Gold, Benjamin J. Raphael |
| 2024 | Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks. Dmitry Kovalev, Ekaterina Borodich, Alexander V. Gasnikov, Dmitrii Feoktistov |
| 2024 | Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization. Rongzhen Wang, Chenyu Zheng, Guoqiang Wu, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li |
| 2024 | LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes. Zefan Qu, Ke Xu, Gerhard P. Hancke, Rynson W. H. Lau |
| 2024 | LucidAction: A Hierarchical and Multi-model Dataset for Comprehensive Action Quality Assessment. Linfeng Dong, Wei Wang, Yu Qiao, Xiao Sun |
| 2024 | Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models. Yang Jiao, Shaoxiang Chen, Zequn Jie, Jingjing Chen, Lin Ma, Yu-Gang Jiang |
| 2024 | Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT. Le Zhuo, Ruoyi Du, Han Xiao, Yangguang Li, Dongyang Liu, Rongjie Huang, Wenze Liu, Xiangyang Zhu, Fu-Yun Wang, Zhanyu Ma, Xu Luo, Zehan Wang, Kaipeng Zhang, Lirui Zhao, Si Liu, Xiangyu Yue, Wanli Ouyang, Yu Qiao, Hongsheng Li, Peng Gao |
| 2024 | M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation. Mingshuang Luo, Ruibing Hou, Zhuo Li, Hong Chang, Zimo Liu, Yaowei Wang, Shiguang Shan |
| 2024 | M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data. Matthew J. Allen, Francisco Dorr, Joseph Alejandro Gallego-Mejia, Laura Martínez-Ferrer, Anna Jungbluth, Freddie Kalaitzis, Raúl Ramos-Pollán |
| 2024 | MAC Advice for facility location mechanism design. Zohar Barak, Anupam Gupta, Inbal Talgam-Cohen |
| 2024 | MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems. Bin Lei, Yi Zhang, Shan Zuo, Ali Payani, Caiwen Ding |
| 2024 | MADiff: Offline Multi-agent Learning with Diffusion Models. Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang |
| 2024 | MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution. Wei Tao, Yucheng Zhou, Yanlin Wang, Wenqiang Zhang, Hongyu Zhang, Yu Cheng |
| 2024 | MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization. Orevaoghene Ahia, Sachin Kumar, Hila Gonen, Valentin Hofmann, Tomasz Limisiewicz, Yulia Tsvetkov, Noah A. Smith |
| 2024 | MALT Powers Up Adversarial Attacks. Odelia Melamed, Gilad Yehudai, Adi Shamir |
| 2024 | MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions. Felix Fent, Fabian Kuttenreich, Florian Ruch, Farija Rizwin, Stefan Juergens, Lorenz Lechermann, Christian Nissler, Andrea Perl, Ulrich Voll, Min Yan, Markus Lienkamp |
| 2024 | MARPLE: A Benchmark for Long-Horizon Inference. Emily Jin, Zhuoyi Huang, Jan-Philipp Fränken, Weiyu Liu, Hannah Cha, Erik Brockbank, Sarah A. Wu, Ruohan Zhang, Jiajun Wu, Tobias Gerstenberg |
| 2024 | MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning. Yifan Jiang, Jiarui Zhang, Kexuan Sun, Zhivar Sourati, Kian Ahrabian, Kaixin Ma, Filip Ilievski, Jay Pujara |
| 2024 | MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models. Zichun Yu, Spandan Das, Chenyan Xiong |
| 2024 | MAmmoTH2: Scaling Instructions from the Web. Xiang Yue, Tianyu Zheng, Ge Zhang, Wenhu Chen |
| 2024 | MC-DiT: Contextual Enhancement via Clean-to-Clean Reconstruction for Masked Diffusion Models. Guanghao Zheng, Yuchen Liu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong |
| 2024 | MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making. Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, Hae Won Park |
| 2024 | MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning. Tieyuan Chen, Huabin Liu, Tianyao He, Yihang Chen, Chaofan Gan, Xiao Ma, Cheng Zhong, Yang Zhang, Yingxue Wang, Hui Lin, Weiyao Lin |
| 2024 | MEQA: A Benchmark for Multi-hop Event-centric Question Answering with Explanations. Ruosen Li, Zimu Wang, Son Quoc Tran, Lei Xia, Xinya Du |
| 2024 | MG-Net: Learn to Customize QAOA with Circuit Depth Awareness. Yang Qian, Xinbiao Wang, Yuxuan Du, Yong Luo, Dacheng Tao |
| 2024 | MGF: Mixed Gaussian Flow for Diverse Trajectory Prediction. Jiahe Chen, Jinkun Cao, Dahua Lin, Kris Kitani, Jiangmiao Pang |
| 2024 | MIDGArD: Modular Interpretable Diffusion over Graphs for Articulated Designs. Quentin Leboutet, Nina Wiedemann, Zhipeng Cai, Michael Paulitsch, Kai Yuan |
| 2024 | MILP-StuDio: MILP Instance Generation via Block Structure Decomposition. Haoyang Liu, Jie Wang, Wanbo Zhang, Zijie Geng, Yufei Kuang, Xijun Li, Bin Li, Yongdong Zhang, Feng Wu |
| 2024 | MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. Anas Awadalla, Le Xue, Oscar Lo, Manli Shu, Hannah Lee, Etash Kumar Guha, Sheng Shen, Mohamed Awadalla, Silvio Savarese, Caiming Xiong, Ran Xu, Yejin Choi, Ludwig Schmidt |
| 2024 | MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention. Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhua Han, Amir H. Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu |
| 2024 | MKGL: Mastery of a Three-Word Language. Lingbing Guo, Zhongpu Bo, Zhuo Chen, Yichi Zhang, Jiaoyan Chen, Yarong Lan, Mengshu Sun, Zhiqiang Zhang, Yangyifei Luo, Qian Li, Qiang Zhang, Wen Zhang, Huajun Chen |
| 2024 | MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs. Jihyung Kil, Zheda Mai, Justin Lee, Arpita Chowdhury, Zihe Wang, Kerrie Cheng, Lemeng Wang, Ye Liu, Wei-Lun Chao |
| 2024 | MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models. Tianle Gu, Zeyang Zhou, Kexin Huang, Dandan Liang, Yixu Wang, Haiquan Zhao, Yuanqi Yao, Xingge Qiao, Keqing Wang, Yujiu Yang, Yan Teng, Yu Qiao, Yingchun Wang |
| 2024 | MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset. Xin Shen, Heming Du, Hongwei Sheng, Shuyun Wang, Hui Chen, Huiqiang Chen, Zhuojie Wu, Xiaobiao Du, Jiaying Ying, Ruihan Lu, Qingzheng Xu, Xin Yu |
| 2024 | MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding. Xinyu Fang, Kangrui Mao, Haodong Duan, Xiangyu Zhao, Yining Li, Dahua Lin, Kai Chen |
| 2024 | MMDU: A Multi-Turn Multi-Image Dialog Understanding Benchmark and Instruction-Tuning Dataset for LVLMs. Ziyu Liu, Tao Chu, Yuhang Zang, Xilin Wei, Xiaoyi Dong, Pan Zhang, Zijian Liang, Yuanjun Xiong, Yu Qiao, Dahua Lin, Jiaqi Wang |
| 2024 | MMLONGBENCH-DOC: Benchmarking Long-context Document Understanding with Visualizations. Yubo Ma, Yuhang Zang, Liangyu Chen, Meiqi Chen, Yizhu Jiao, Xinze Li, Xinyuan Lu, Ziyu Liu, Yan Ma, Xiaoyi Dong, Pan Zhang, Liangming Pan, Yu-Gang Jiang, Jiaqi Wang, Yixin Cao, Aixin Sun |
| 2024 | MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark. Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, Wenhu Chen |
| 2024 | MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation. Jialin Luo, Yuanzhi Wang, Ziqi Gu, Yide Qiu, Shuaizhen Yao, Fuyun Wang, Chunyan Xu, Wenhua Zhang, Dan Wang, Zhen Cui |
| 2024 | MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations. Ruiyuan Lyu, Jingli Lin, Tai Wang, Shuai Yang, Xiaohan Mao, Yilun Chen, Runsen Xu, Haifeng Huang, Chenming Zhu, Dahua Lin, Jiangmiao Pang |
| 2024 | MMSite: A Multi-modal Framework for the Identification of Active Sites in Proteins. Song Ouyang, Huiyu Cai, Yong Luo, Kehua Su, Lefei Zhang, Bo Du |
| 2024 | MO-DDN: A Coarse-to-Fine Attribute-based Exploration Agent for Multi-Object Demand-driven Navigation. Hongcheng Wang, Peiqi Liu, Wenzhe Cai, Mingdong Wu, Zhengyu Qian, Hao Dong |
| 2024 | MOTE-NAS: Multi-Objective Training-based Estimate for Efficient Neural Architecture Search. Yuming Zhang, Jun-Wei Hsieh, Xin Li, Ming-Ching Chang, Chun-Chieh Lee, Kuo-Chin Fan |
| 2024 | MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction. John Arevalo, Ellen Su, Anne E. Carpenter, Shantanu Singh |
| 2024 | MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs. Zhongshen Zeng, Yinhong Liu, Yingjia Wan, Jingyao Li, Pengguang Chen, Jianbo Dai, Yuxuan Yao, Rongwu Xu, Zehan Qi, Wanru Zhao, Linling Shen, Jianqiao Lu, Haochen Tan, Yukang Chen, Hao Zhang, Zhan Shi, Bailin Wang, Zhijiang Guo, Jiaya Jia |
| 2024 | MSA Generation with Seqs2Seqs Pretraining: Advancing Protein Structure Predictions. Le Zhang, Jiayang Chen, Tao Shen, Yu Li, Siqi Sun |
| 2024 | MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training. Bo Chen, Zhilei Bei, Xingyi Cheng, Pan Li, Jie Tang, Le Song |
| 2024 | MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution. Wenzhuo Liu, Fei Zhu, Shijie Ma, Cheng-Lin Liu |
| 2024 | MTGS: A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction. Anshul Gupta, Samy Tafasca, Arya Farkhondeh, Pierre Vuillecard, Jean-Marc Odobez |
| 2024 | MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encoding. Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni |
| 2024 | MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images. Eunji Hong, Minh Hieu Nguyen, Mikaela Angelina Uy, Minhyuk Sung |
| 2024 | MVGamba: Unify 3D Content Generation as State Space Sequence Modeling. Xuanyu Yi, Zike Wu, Qiuhong Shen, Qingshan Xu, Pan Zhou, Joo-Hwee Lim, Shuicheng Yan, Xinchao Wang, Hanwang Zhang |
| 2024 | MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing. Chenjie Cao, Chaohui Yu, Fan Wang, Xiangyang Xue, Yanwei Fu |
| 2024 | MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps. Yating Xu, Chen Li, Gim Hee Lee |
| 2024 | MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views. Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, Jianfei Cai |
| 2024 | MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts. Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Weijian Deng, Jianfeng Zhang, Bo An |
| 2024 | MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model. Chaoya Jiang, Hongrui Jia, Haiyang Xu, Wei Ye, Mengfan Dong, Ming Yan, Ji Zhang, Fei Huang, Shikun Zhang |
| 2024 | MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization. Aozhong Zhang, Naigang Wang, Yanxia Deng, Xin Li, Zi Yang, Penghang Yin |
| 2024 | Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function. Chenyi Zhuang, Ying Hu, Pan Gao |
| 2024 | Maia-2: A Unified Model for Human-AI Alignment in Chess. Zhenwei Tang, Difan Jiao, Reid McIlroy-Young, Jon M. Kleinberg, Siddhartha Sen, Ashton Anderson |
| 2024 | Make Continual Learning Stronger via C-Flat. Ang Bian, Wei Li, Hangjie Yuan, Chengrong Yu, Mang Wang, Zixiang Zhao, Aojun Lu, Pengliang Ji, Tao Feng |
| 2024 | Make Your LLM Fully Utilize the Context. Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, Weizhu Chen |
| 2024 | Make-An-Agent: A Generalizable Policy Network Generator with Behavior-Prompted Diffusion. Yongyuan Liang, Tingqiang Xu, Kaizhe Hu, Guangqi Jiang, Furong Huang, Huazhe Xu |
| 2024 | Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials. Ye Fang, Zeyi Sun, Tong Wu, Jiaqi Wang, Ziwei Liu, Gordon Wetzstein, Dahua Lin |
| 2024 | Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning. Qi Wang, Junming Yang, Yunbo Wang, Xin Jin, Wenjun Zeng, Xiaokang Yang |
| 2024 | MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection. Haoyang He, Yuhu Bai, Jiangning Zhang, Qingdong He, Hongxu Chen, Zhenye Gan, Chengjie Wang, Xiangtai Li, Guanzhong Tian, Lei Xie |
| 2024 | MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space. Jiangwei Weng, Zhiqiang Yan, Ying Tai, Jianjun Qian, Jian Yang, Jun Li |
| 2024 | MambaLRP: Explaining Selective State Space Sequence Models. Farnoush Rezaei Jafari, Grégoire Montavon, Klaus-Robert Müller, Oliver Eberle |
| 2024 | MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging. Zhenghao Pan, Haijin Zeng, Jiezhang Cao, Yongyong Chen, Kai Zhang, Yong Xu |
| 2024 | MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models. Zunnan Xu, Yukang Lin, Haonan Han, Sicheng Yang, Ronghui Li, Yachao Zhang, Xiu Li |
| 2024 | MambaTree: Tree Topology is All You Need in State Space Model. Yicheng Xiao, Lin Song, Shaoli Huang, Jiangshan Wang, Siyu Song, Yixiao Ge, Xiu Li, Ying Shan |
| 2024 | ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation. Cédric Rommel, Victor Letzelter, Nermin Samet, Renaud Marlet, Matthieu Cord, Patrick Pérez, Eduardo Valle |
| 2024 | Many-Shot In-Context Learning. Rishabh Agarwal, Avi Singh, Lei Zhang, Bernd Bohnet, Luis Rosias, Stephanie C. Y. Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle |
| 2024 | Many-shot Jailbreaking. Cem Anil, Esin Durmus, Nina Panickssery, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Meg Tong, Jesse Mu, Daniel Ford, Francesco Mosconi, Rajashree Agrawal, Rylan Schaeffer, Naomi Bashkansky, Samuel Svenningsen, Mike Lambert, Ansh Radhakrishnan, Carson Denison, Evan Hubinger, Yuntao Bai, Trenton Bricken, Timothy Maxwell, Nicholas Schiefer, James Sully, Alex Tamkin, Tamera Lanham, Karina Nguyen, Tomek Korbak, Jared Kaplan, Deep Ganguli, Samuel R. Bowman, Ethan Perez, Roger B. Grosse, David Kristjanson Duvenaud |
| 2024 | Map It Anywhere: Empowering BEV Map Prediction using Large-scale Public Datasets. Cherie Ho, Jiaye Zou, Omar Alama, Sai Mitheran Jagadesh Kumar, Cheng-Yu Chiang, Taneesh Gupta, Chen Wang, Nikhil Varma Keetha, Katia P. Sycara, Sebastian A. Scherer |
| 2024 | Marginal Causal Flows for Validation and Inference. Daniel de Vassimon Manela, Laura Battaglia, Robin J. Evans |
| 2024 | Markov Equivalence and Consistency in Differentiable Structure Learning. Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam |
| 2024 | Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows. Alberto Cabezas, Louis Sharrock, Christopher Nemeth |
| 2024 | Marrying Causal Representation Learning with Dynamical Systems for Science. Dingling Yao, Caroline Muller, Francesco Locatello |
| 2024 | Mars: Situated Inductive Reasoning in an Open-World Environment. Xiaojuan Tang, Jiaqi Li, Yitao Liang, Song-Chun Zhu, Muhan Zhang, Zilong Zheng |
| 2024 | MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation. Haotian Qian, Yinda Chen, Shengtao Lou, Fahad Shahbaz Khan, Xiaogang Jin, Deng-Ping Fan |
| 2024 | MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models. Gongfan Fang, Hongxu Yin, Saurav Muralidharan, Greg Heinrich, Jeff Pool, Jan Kautz, Pavlo Molchanov, Xinchao Wang |
| 2024 | Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages. Andy Yang, David Chiang, Dana Angluin |
| 2024 | Masked Pre-training Enables Universal Zero-shot Denoiser. Xiaoxiao Ma, Zhixiang Wei, Yi Jin, Pengyang Ling, Tianle Liu, Ben Wang, Junkang Dai, Huaian Chen |
| 2024 | MassSpecGym: A benchmark for the discovery and identification of molecules. Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A. Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo Wang, David S. Wishart, Liping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D. Mak, Soha Hassoun, Florian Huber, Justin J. J. van der Hooft, Michael A. Stravs, Sebastian Böcker, Josef Sivic, Tomás Pluskal |
| 2024 | MatFormer: Nested Transformer for Elastic Inference. Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hanna Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain |
| 2024 | Matching the Statistical Query Lower Bound for k-Sparse Parity Problems with Sign Stochastic Gradient Descent. Yiwen Kou, Zixiang Chen, Quanquan Gu, Sham M. Kakade |
| 2024 | MathPile: A Billion-Token-Scale Pretraining Corpus for Math. Zengzhi Wang, Xuefeng Li, Rui Xia, Pengfei Liu |
| 2024 | Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods. Yihan Zhang, Marco Mondelli |
| 2024 | MatrixNet: Learning over symmetry groups using learned group representations. Lucas Laird, Circe Hsu, Asilata Bapat, Robin Walters |
| 2024 | Matryoshka Query Transformer for Large Vision-Language Models. Wenbo Hu, Zi-Yi Dou, Liunian Harold Li, Amita Kamath, Nanyun Peng, Kai-Wei Chang |
| 2024 | Maximizing utility in multi-agent environments by anticipating the behavior of other learners. Angelos Assos, Yuval Dagan, Constantinos Daskalakis |
| 2024 | Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models. Sangwoong Yoon, Himchan Hwang, Dohyun Kwon, Yung-Kyun Noh, Frank C. Park |
| 2024 | Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow. Chen-Hao Chao, Chien Feng, Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee |
| 2024 | Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs. Rudolf Laine, Bilal Chughtai, Jan Betley, Kaivalya Hariharan, Mikita Balesni, Jérémy Scheurer, Marius Hobbhahn, Alexander Meinke, Owain Evans |
| 2024 | MeLLoC: Lossless Compression with High-order Mechanism Learning. Xinyue Luo, Jin Cheng, Yu Chen |
| 2024 | MeMo: Meaningful, Modular Controllers via Noise Injection. Megan Tjandrasuwita, Jie Xu, Armando Solar-Lezama, Wojciech Matusik |
| 2024 | Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input. Ziang Chen, Rong Ge |
| 2024 | Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach. Guillaume Wang, Alireza Mousavi Hosseini, Lénaïc Chizat |
| 2024 | Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance. Kai Xiong, Xiao Ding, Ting Liu, Bing Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Yixin Cao |
| 2024 | Measuring Dejavu Memorization Efficiently. Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri |
| 2024 | Measuring Goal-Directedness. Matt MacDermott, James Fox, Francesco Belardinelli, Tom Everitt |
| 2024 | Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset. Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, Hongsheng Li |
| 2024 | Measuring Mutual Policy Divergence for Multi-Agent Sequential Exploration. Haowen Dou, Lujuan Dang, Zhirong Luan, Badong Chen |
| 2024 | Measuring Per-Unit Interpretability at Scale Without Humans. Roland S. Zimmermann, David A. Klindt, Wieland Brendel |
| 2024 | Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models. Adam Karvonen, Benjamin Wright, Can Rager, Rico Angell, Jannik Brinkmann, Logan Smith, Claudio Mayrink Verdun, David Bau, Samuel Marks |
| 2024 | Mechanism design augmented with output advice. George Christodoulou, Alkmini Sgouritsa, Ioannis Vlachos |
| 2024 | Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learning. Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang, Anthony Philippakis, David A. Sontag, Ahmed M. Alaa |
| 2024 | MedCalc-Bench: Evaluating Large Language Models for Medical Calculations. Nikhil Khandekar, Qiao Jin, Guangzhi Xiong, Soren Dunn, Serina S. Applebaum, Zain Anwar, Maame Sarfo-Gyamfi, Conrad W. Safranek, Abid A Anwar, Andrew Zhang, Aidan Gilson, Maxwell B. Singer, Amisha D. Dave, Andrew Taylor, Aidong Zhang, Qingyu Chen, Zhiyong Lu |
| 2024 | MedJourney: Benchmark and Evaluation of Large Language Models over Patient Clinical Journey. Xian Wu, Yutian Zhao, Yunyan Zhang, Jiageng Wu, Zhihong Zhu, Yingying Zhang, Yi Ouyang, Ziheng Zhang, Huimin Wang, Zhenxi Lin, Jie Yang, Shuang Zhao, Yefeng Zheng |
| 2024 | MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Models. Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju |
| 2024 | Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification. Yihe Wang, Nan Huang, Taida Li, Yujun Yan, Xiang Zhang |
| 2024 | MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning. Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, Jonathan Ilgen, Emma Pierson, Pang Wei W. Koh, Yulia Tsvetkov |
| 2024 | Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length. Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong, Beidi Chen, Lili Yu, Hao Zhang, Jonathan May, Luke Zettlemoyer, Omer Levy, Chunting Zhou |
| 2024 | Melting Pot Contest: Charting the Future of Generalized Cooperative Intelligence. Rakshit S. Trivedi, Akbir Khan, Jesse Clifton, Lewis Hammond, Edgar A. Duéñez-Guzmán, Dipam Chakraborty, John P. Agapiou, Jayd Matyas, Alexander Sasha Vezhnevets, Barna Pásztor, Yunke Ao, Omar G. Younis, Jiawei Huang, Benjamin Swain, Haoyuan Qin, Mian Deng, Ziwei Deng, Utku Erdoganaras, Yue Zhao, Marko Tesic, Natasha Jaques, Jakob N. Foerster, Vincent Conitzer, José Hernández-Orallo, Dylan Hadfield-Menell, Joel Z. Leibo |
| 2024 | MemVLT: Vision-Language Tracking with Adaptive Memory-based Prompts. Xiaokun Feng, Xuchen Li, Shiyu Hu, Dailing Zhang, Meiqi Wu, Jing Zhang, Xiaotang Chen, Kaiqi Huang |
| 2024 | Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration. Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang |
| 2024 | Membership Inference Attacks against Large Vision-Language Models. Zhan Li, Yongtao Wu, Yihang Chen, Francesco Tonin, Elías Abad-Rocamora, Volkan Cevher |
| 2024 | Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy. Shengfang Zhai, Huanran Chen, Yinpeng Dong, Jiajun Li, Qingni Shen, Yansong Gao, Hang Su, Yang Liu |
| 2024 | Memorize What Matters: Emergent Scene Decomposition from Multitraverse. Yiming Li, Zehong Wang, Yue Wang, Zhiding Yu, Zan Gojcic, Marco Pavone, Chen Feng, José M. Álvarez |
| 2024 | Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization. Qianli Shen, Yezhen Wang, Zhouhao Yang, Xiang Li, Haonan Wang, Yang Zhang, Jonathan Scarlett, Zhanxing Zhu, Kenji Kawaguchi |
| 2024 | Memory-Efficient LLM Training with Online Subspace Descent. Kaizhao Liang, Bo Liu, Lizhang Chen, Qiang Liu |
| 2024 | MemoryFormer : Minimize Transformer Computation by Removing Fully-Connected Layers. Ning Ding, Yehui Tang, Haochen Qin, Zhenli Zhou, Chao Xu, Lin Li, Kai Han, Heng Liao, Yunhe Wang |
| 2024 | Mercury: A Code Efficiency Benchmark for Code Large Language Models. Mingzhe Du, Anh Tuan Luu, Bin Ji, Qian Liu, See-Kiong Ng |
| 2024 | Mesa-Extrapolation: A Weave Position Encoding Method for Enhanced Extrapolation in LLMs. Xin Ma, Yang Liu, Jingjing Liu, Xiaoxu Ma |
| 2024 | MeshFormer : High-Quality Mesh Generation with 3D-Guided Reconstruction Model. Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, Hongzhi Wu, Hao Su |
| 2024 | MeshXL: Neural Coordinate Field for Generative 3D Foundation Models. Sijin Chen, Xin Chen, Anqi Pang, Xianfang Zeng, Wei Cheng, Yijun Fu, Fukun Yin, Billzb Wang, Jingyi Yu, Gang Yu, Bin Fu, Tao Chen |
| 2024 | Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials. Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahendra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, David Novotný |
| 2024 | Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control. Seongwoong Cho, Donggyun Kim, Jinwoo Lee, Seunghoon Hong |
| 2024 | Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement. Zhi Wang, Li Zhang, Wenhao Wu, Yuanheng Zhu, Dongbin Zhao, Chunlin Chen |
| 2024 | Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-Exploration. Yun-Yen Chuang, Hung-Min Hsu, Kevin Lin, Chen-Sheng Gu, Ling Zhen Li, Ray-I Chang, Hung-yi Lee |
| 2024 | Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning. Fei Zhou, Peng Wang, Lei Zhang, Zhenghua Chen, Wei Wei, Chen Ding, Guosheng Lin, Yanning Zhang |
| 2024 | Meta-Learning Universal Priors Using Non-Injective Change of Variables. Yilang Zhang, Alireza Sadeghi, Georgios B. Giannakis |
| 2024 | Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator. Siyuan Xu, Minghui Zhu |
| 2024 | MetaAligner: Towards Generalizable Multi-Objective Alignment of Language Models. Kailai Yang, Zhiwei Liu, Qianqian Xie, Jimin Huang, Tianlin Zhang, Sophia Ananiadou |
| 2024 | MetaCURL: Non-stationary Concave Utility Reinforcement Learning. Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard, Nadia Oudjane |
| 2024 | MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map. Yuhong Chou, Man Yao, Kexin Wang, Yuqi Pan, Rui-Jie Zhu, Jibin Wu, Yiran Zhong, Yu Qiao, Bo Xu, Guoqi Li |
| 2024 | MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning. Bin-Bin Gao |
| 2024 | Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo Jimenez Rezende, Yoshua Bengio, Michael C. Mozer, Sanjeev Arora |
| 2024 | Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models. Byung-Kwan Lee, Chae Won Kim, Beomchan Park, Yong Man Ro |
| 2024 | Metric Flow Matching for Smooth Interpolations on the Data Manifold. Kacper Kapusniak, Peter Potaptchik, Teodora Reu, Leo Zhang, Alexander Tong, Michael M. Bronstein, Avishek Joey Bose, Francesco Di Giovanni |
| 2024 | Metric Space Magnitude for Evaluating the Diversity of Latent Representations. Katharina Limbeck, Rayna Andreeva, Rik Sarkar, Bastian Rieck |
| 2024 | Metric Transforms and Low Rank Representations of Kernels for Fast Attention. Timothy Chu, Josh Alman, Gary L. Miller, Shyam Narayanan, Mark Sellke, Zhao Song |
| 2024 | Metric from Human: Zero-shot Monocular Metric Depth Estimation via Test-time Adaptation. Yizhou Zhao, Hengwei Bian, Kaihua Chen, Pengliang Ji, Liao Qu, Shao-yu Lin, Weichen Yu, Haoran Li, Hao Chen, Jun Shen, Bhiksha Raj, Min Xu |
| 2024 | MiSO: Optimizing brain stimulation to create neural activity states. Yuki Minai, Joana Soldado-Magraner, Matthew A. Smith, Byron M. Yu |
| 2024 | Micro-Bench: A Microscopy Benchmark for Vision-Language Understanding. Alejandro Lozano, Jeffrey J. Nirschl, James Burgess, Sanket Rajan Gupte, Yuhui Zhang, Alyssa Unell, Serena Yeung |
| 2024 | MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh |
| 2024 | Microstructures and Accuracy of Graph Recall by Large Language Models. Yanbang Wang, Hejie Cui, Jon M. Kleinberg |
| 2024 | MimicTalk: Mimicking a personalized and expressive 3D talking face in minutes. Zhenhui Ye, Tianyun Zhong, Yi Ren, Ziyue Jiang, Jiawei Huang, Rongjie Huang, Jinglin Liu, Jinzheng He, Chen Zhang, Zehan Wang, Xize Cheng, Xiang Yin, Zhou Zhao |
| 2024 | Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Games. The Viet Bui, Tien Anh Mai, Thanh Hong Nguyen |
| 2024 | Mind the Gap Between Prototypes and Images in Cross-domain Finetuning. Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han |
| 2024 | Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making. Drago Plecko, Elias Bareinboim |
| 2024 | Mind the Graph When Balancing Data for Fairness or Robustness. Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa |
| 2024 | Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models. Wenshan Wu, Shaoguang Mao, Yadong Zhang, Yan Xia, Li Dong, Lei Cui, Furu Wei |
| 2024 | MindMerger: Efficiently Boosting LLM Reasoning in non-English Languages. Zixian Huang, Wenhao Zhu, Gong Cheng, Lei Li, Fei Yuan |
| 2024 | Mini-Sequence Transformers: Optimizing Intermediate Memory for Long Sequences Training. Cheng Luo, Jiawei Zhao, Zhuoming Chen, Beidi Chen, Animashree Anandkumar |
| 2024 | MiniCache: KV Cache Compression in Depth Dimension for Large Language Models. Akide Liu, Jing Liu, Zizheng Pan, Yefei He, Reza Haffari, Bohan Zhuang |
| 2024 | Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning. Zhishuai Liu, Pan Xu |
| 2024 | Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization. Zheyi Fan, Wenyu Wang, Szu Hui Ng, Qingpei Hu |
| 2024 | Minimum Entropy Coupling with Bottleneck. M. Reza Ebrahimi, Jun Chen, Ashish Khisti |
| 2024 | Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration. Kezheng Xiong, Haoen Xiang, Qingshan Xu, Chenglu Wen, Siqi Shen, Jonathan Jun Li, Cheng Wang |
| 2024 | MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions. Xuan Ju, Yiming Gao, Zhaoyang Zhang, Ziyang Yuan, Xintao Wang, Ailing Zeng, Yu Xiong, Qiang Xu, Ying Shan |
| 2024 | Mirror and Preconditioned Gradient Descent in Wasserstein Space. Clément Bonet, Théo Uscidda, Adam David, Pierre-Cyril Aubin-Frankowski, Anna Korba |
| 2024 | Mission Impossible: A Statistical Perspective on Jailbreaking LLMs. Jingtong Su, Julia Kempe, Karen Ullrich |
| 2024 | Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor. Shaokui Wei, Hongyuan Zha, Baoyuan Wu |
| 2024 | Mitigating Biases in Blackbox Feature Extractors for Image Classification Tasks. Abhipsa Basu, Saswat Subhajyoti Mallick, R. Venkatesh Babu |
| 2024 | Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction. Seokin Seo, Byung-Jun Lee, Jongmin Lee, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim |
| 2024 | Mitigating Object Hallucination via Concentric Causal Attention. Yun Xing, Yiheng Li, Ivan Laptev, Shijian Lu |
| 2024 | Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris |
| 2024 | Mitigating Reward Overoptimization via Lightweight Uncertainty Estimation. Xiaoying Zhang, Jean-Francois Ton, Wei Shen, Hongning Wang, Yang Liu |
| 2024 | Mitigating Spurious Correlations via Disagreement Probability. Hyeonggeun Han, Sehwan Kim, Hyungjun Joo, Sangwoo Hong, Jungwoo Lee |
| 2024 | MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures. Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You |
| 2024 | Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes. Zhenfeng Tu, Santiago Aranguri, Arthur Jacot |
| 2024 | Mixture of Adversarial LoRAs: Boosting Robust Generalization in Meta-Tuning. Xu Yang, Chen Liu, Ying Wei |
| 2024 | Mixture of Demonstrations for In-Context Learning. Song Wang, Zihan Chen, Chengshuai Shi, Cong Shen, Jundong Li |
| 2024 | Mixture of Experts Meets Prompt-Based Continual Learning. Minh Le, An Nguyen The, Huy Nguyen, Trang Nguyen, Trang Pham, Linh Ngo Van, Nhat Ho |
| 2024 | Mixture of In-Context Experts Enhance LLMs' Long Context Awareness. Hongzhan Lin, Ang Lv, Yuhan Chen, Chen Zhu, Yang Song, Hengshu Zhu, Rui Yan |
| 2024 | Mixture of Link Predictors on Graphs. Li Ma, Haoyu Han, Juanhui Li, Harry Shomer, Hui Liu, Xiaofeng Gao, Jiliang Tang |
| 2024 | Mixture of Nested Experts: Adaptive Processing of Visual Tokens. Gagan Jain, Nidhi Hegde, Aditya Kusupati, Arsha Nagrani, Shyamal Buch, Prateek Jain, Anurag Arnab, Sujoy Paul |
| 2024 | Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models. Dongwon Jo, Taesu Kim, Yulhwa Kim, Jae-Joon Kim |
| 2024 | Mixture of Tokens: Continuous MoE through Cross-Example Aggregation. Szymon Antoniak, Michal Krutul, Maciej Pióro, Jakub Krajewski, Jan Ludziejewski, Kamil Ciebiera, Krystian Król, Tomasz Odrzygózdz, Marek Cygan, Sebastian Jaszczur |
| 2024 | Mixture of neural fields for heterogeneous reconstruction in cryo-EM. Axel Levy, Rishwanth Raghu, David Shustin, Adele Rui-Yang Peng, Huan Li, Oliver Biggs Clarke, Gordon Wetzstein, Ellen D. Zhong |
| 2024 | Mixtures of Experts for Audio-Visual Learning. Ying Cheng, Yang Li, Junjie He, Rui Feng |
| 2024 | MmCows: A Multimodal Dataset for Dairy Cattle Monitoring. Hien Vu, Omkar Prabhune, Unmesh Raskar, Dimuth Panditharatne, Hanwook Chung, Christopher Y. Choi, Younghyun Kim |
| 2024 | MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Tasks. Xingkui Zhu, Yiran Guan, Dingkang Liang, Yuchao Chen, Yuliang Liu, Xiang Bai |
| 2024 | MoEUT: Mixture-of-Experts Universal Transformers. Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber, Christopher Potts, Christopher D. Manning |
| 2024 | MoGU: A Framework for Enhancing Safety of LLMs While Preserving Their Usability. Yanrui Du, Sendong Zhao, Danyang Zhao, Ming Ma, Yuhan Chen, Liangyu Huo, Qing Yang, Dongliang Xu, Bing Qin |
| 2024 | MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling. Weihao Yuan, Yisheng He, Weichao Shen, Yuan Dong, Xiaodong Gu, Zilong Dong, Liefeng Bo, Qixing Huang |
| 2024 | MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Experts. Jie Zhu, Yixiong Chen, Mingyu Ding, Ping Luo, Leye Wang, Jingdong Wang |
| 2024 | MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models. Leyang Shen, Gongwei Chen, Rui Shao, Weili Guan, Liqiang Nie |
| 2024 | MoMu-Diffusion: On Learning Long-Term Motion-Music Synchronization and Correspondence. Fuming You, Minghui Fang, Li Tang, Rongjie Huang, Yongqi Wang, Zhou Zhao |
| 2024 | MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer. Minghao Zhu, Zhengpu Wang, Mengxian Hu, Ronghao Dang, Xiao Lin, Xun Zhou, Chengju Liu, Qijun Chen |
| 2024 | MoVA: Adapting Mixture of Vision Experts to Multimodal Context. Zhuofan Zong, Bingqi Ma, Dazhong Shen, Guanglu Song, Hao Shao, Dongzhi Jiang, Hongsheng Li, Yu Liu |
| 2024 | Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration. Junyang Wang, Haiyang Xu, Haitao Jia, Xi Zhang, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, Jitao Sang |
| 2024 | Mobility-LLM: Learning Visiting Intentions and Travel Preference from Human Mobility Data with Large Language Models. Letian Gong, Yan Lin, Xinyue Zhang, Yiwen Lu, Xuedi Han, Yichen Liu, Shengnan Guo, Youfang Lin, Huaiyu Wan |
| 2024 | Model Based Inference of Synaptic Plasticity Rules. Yash Mehta, Danil Tyulmankov, Adithya Rajagopalan, Glenn Turner, James FitzGerald, Jan Funke |
| 2024 | Model Collapse Demystified: The Case of Regression. Elvis Dohmatob, Yunzhen Feng, Julia Kempe |
| 2024 | Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA. Lifeng Qiao, Peng Ye, Yuchen Ren, Weiqiang Bai, Chaoqi Liang, Xinzhu Ma, Nanqing Dong, Wanli Ouyang |
| 2024 | Model Fusion through Bayesian Optimization in Language Model Fine-Tuning. Chaeyun Jang, Hyungi Lee, Jungtaek Kim, Juho Lee |
| 2024 | Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks. Jiacong Hu, Jing Gao, Jingwen Ye, Yang Gao, Xingen Wang, Zunlei Feng, Mingli Song |
| 2024 | Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory. Pasan Dissanayake, Sanghamitra Dutta |
| 2024 | Model Sensitivity Aware Continual Learning. Zhenyi Wang, Heng Huang |
| 2024 | Model-Based Transfer Learning for Contextual Reinforcement Learning. Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu |
| 2024 | Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild. Xinyu Zhao, Guoheng Sun, Ruisi Cai, Yukun Zhou, Pingzhi Li, Peihao Wang, Bowen Tan, Yexiao He, Li Chen, Yi Liang, Beidi Chen, Binhang Yuan, Hongyi Wang, Ang Li, Zhangyang Wang, Tianlong Chen |
| 2024 | Model-based Diffusion for Trajectory Optimization. Chaoyi Pan, Zeji Yi, Guanya Shi, Guannan Qu |
| 2024 | Model-free Low-Rank Reinforcement Learning via Leveraged Entry-wise Matrix Estimation. Stefan Stojanovic, Yassir Jedra, Alexandre Proutière |
| 2024 | Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems. Amber Hu, David M. Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott W. Linderman |
| 2024 | Molecule Design by Latent Prompt Transformer. Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu |
| 2024 | Molecule Generation with Fragment Retrieval Augmentation. Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Saee Paliwal, Arash Vahdat, Weili Nie |
| 2024 | MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts. Rachel S. Y. Teo, Tan Nguyen |
| 2024 | MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity. Lynn Le, Paolo Papale, Katja Seeliger, Antonio Lozano, Thirza Dado, Feng Wang, Pieter R. Roelfsema, Marcel A. J. van Gerven, Yagmur Güçlütürk, Umut Güçlü |
| 2024 | MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders. Xueying Jiang, Sheng Jin, Xiaoqin Zhang, Ling Shao, Shijian Lu |
| 2024 | Monoculture in Matching Markets. Kenny Peng, Nikhil Garg |
| 2024 | Monomial Matrix Group Equivariant Neural Functional Networks. Hoang V. Tran, Thieu N. Vo, Tho Huu, An Nguyen The, Tan Minh Nguyen |
| 2024 | Monte Carlo Tree Search based Space Transfer for Black Box Optimization. Shukuan Wang, Ke Xue, Lei Song, Xiaobin Huang, Chao Qian |
| 2024 | Most Influential Subset Selection: Challenges, Promises, and Beyond. Yuzheng Hu, Pingbang Hu, Han Zhao, Jiaqi W. Ma |
| 2024 | Motif-oriented influence maximization for viral marketing in large-scale social networks. Mingyang Zhou, Weiji Cao, Hao Liao, Rui Mao |
| 2024 | Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation. Yuanhao Zhai, Kevin Lin, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Chung-Ching Lin, David S. Doermann, Junsong Yuan, Lijuan Wang |
| 2024 | Motion Forecasting in Continuous Driving. Nan Song, Bozhou Zhang, Xiatian Zhu, Li Zhang |
| 2024 | Motion Graph Unleashed: A Novel Approach to Video Prediction. Yiqi Zhong, Luming Liang, Bohan Tang, Ilya Zharkov, Ulrich Neumann |
| 2024 | MotionBooth: Motion-Aware Customized Text-to-Video Generation. Jianzong Wu, Xiangtai Li, Yanhong Zeng, Jiangning Zhang, Qianyu Zhou, Yining Li, Yunhai Tong, Kai Chen |
| 2024 | MotionCraft: Physics-Based Zero-Shot Video Generation. Antonio Montanaro, Luca Savant Aira, Emanuele Aiello, Diego Valsesia, Enrico Magli |
| 2024 | MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting. Ruijie Zhu, Yanzhe Liang, Hanzhi Chang, Jiacheng Deng, Jiahao Lu, Wenfei Yang, Tianzhu Zhang, Yongdong Zhang |
| 2024 | MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI. Tobit Klug, Kun Wang, Stefan Ruschke, Reinhard Heckel |
| 2024 | Moving Off-the-Grid: Scene-Grounded Video Representations. Sjoerd van Steenkiste, Daniel Zoran, Yi Yang, Yulia Rubanova, Rishabh Kabra, Carl Doersch, Dilara Gokay, Joseph Heyward, Etienne Pot, Klaus Greff, Drew A. Hudson, Thomas Keck, João Carreira, Alexey Dosovitskiy, Mehdi S. M. Sajjadi, Thomas Kipf |
| 2024 | Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition. Mehreen Saeed, Adrian Chan, Anupam Mijar, Joseph Moukarzel, Georges Habchi, Carlos Younes, Amin Elias, Chau-Wai Wong, Akram Khater |
| 2024 | Multi-Agent Coordination via Multi-Level Communication. Gang Ding, Zeyuan Liu, Zhirui Fang, Kefan Su, Liwen Zhu, Zongqing Lu |
| 2024 | Multi-Agent Domain Calibration with a Handful of Offline Data. Tao Jiang, Lei Yuan, Lihe Li, Cong Guan, Zongzhang Zhang, Yang Yu |
| 2024 | Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu |
| 2024 | Multi-Chain Graphs of Graphs: A New Approach to Analyzing Blockchain Datasets. Bingqiao Luo, Zhen Zhang, Qian Wang, Bingsheng He |
| 2024 | Multi-Group Proportional Representation in Retrieval. Alex Oesterling, Claudio Mayrink Verdun, Alexander Glynn, Carol Xuan Long, Lucas Monteiro Paes, Sajani Vithana, Martina Cardone, Flávio P. Calmon |
| 2024 | Multi-Head Mixture-of-Experts. Xun Wu, Shaohan Huang, Wenhui Wang, Shuming Ma, Li Dong, Furu Wei |
| 2024 | Multi-Instance Partial-Label Learning with Margin Adjustment. Wei Tang, Yin-Fang Yang, Zhaofei Wang, Weijia Zhang, Min-Ling Zhang |
| 2024 | Multi-LLM Debate: Framework, Principals, and Interventions. Andrew Estornell, Yang Liu |
| 2024 | Multi-Label Learning with Stronger Consistency Guarantees. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2024 | Multi-Label Open Set Recognition. Yibo Wang, Jun-Yi Hang, Min-Ling Zhang |
| 2024 | Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention. Haomeng Zhang, Chiao-An Yang, Raymond A. Yeh |
| 2024 | Multi-Object Hallucination in Vision Language Models. Xuweiyi Chen, Ziqiao Ma, Xuejun Zhang, Sihan Xu, Shengyi Qian, Jianing Yang, David Fouhey, Joyce Chai |
| 2024 | Multi-Reward Best Policy Identification. Alessio Russo, Filippo Vannella |
| 2024 | Multi-Scale Representation Learning for Protein Fitness Prediction. Zuobai Zhang, Pascal Notin, Yining Huang, Aurélie C. Lozano, Vijil Chenthamarakshan, Debora S. Marks, Payel Das, Jian Tang |
| 2024 | Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model. Yuheng Shi, Minjing Dong, Chang Xu |
| 2024 | Multi-Stage Predict+Optimize for (Mixed Integer) Linear Programs. Xinyi Hu, Jasper C. H. Lee, Jimmy H. M. Lee, Peter J. Stuckey |
| 2024 | Multi-Winner Reconfiguration. Jiehua Chen, Christian Hatschka, Sofia Simola |
| 2024 | Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images. Donghwan Kim, Tae-Kyun Kim |
| 2024 | Multi-language Diversity Benefits Autoformalization. Albert Q. Jiang, Wenda Li, Mateja Jamnik |
| 2024 | Multi-modal Situated Reasoning in 3D Scenes. Xiongkun Linghu, Jiangyong Huang, Xuesong Niu, Xiaojian (Shawn) Ma, Baoxiong Jia, Siyuan Huang |
| 2024 | Multi-modal Transfer Learning between Biological Foundation Models. Juan Jose Garau-Luis, Patrick Bordes, Liam Gonzalez, Masa Roller, Bernardo P. de Almeida, Christopher Blum, Lorenz Hexemer, Stefan Laurent, Maren Lang, Thomas Pierrot, Guillaume Richard |
| 2024 | Multi-model Ensemble Conformal Prediction in Dynamic Environments. Erfan Hajihashemi, Yanning Shen |
| 2024 | Multi-scale Consistency for Robust 3D Registration via Hierarchical Sinkhorn Tree. Chengwei Ren, Yifan Feng, Weixiang Zhang, Xiao-Ping (Steven) Zhang, Yue Gao |
| 2024 | Multi-times Monte Carlo Rendering for Inter-reflection Reconstruction. Tengjie Zhu, Zhuo Chen, Jingnan Gao, Yichao Yan, Xiaokang Yang |
| 2024 | Multi-turn Reinforcement Learning with Preference Human Feedback. Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, Avinatan Hassidim, Yossi Matias, Rémi Munos |
| 2024 | Multi-view Masked Contrastive Representation Learning for Endoscopic Video Analysis. Kai Hu, Ye Xiao, Yuan Zhang, Xieping Gao |
| 2024 | MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities. Hao Dong, Yue Zhao, Eleni N. Chatzi, Olga Fink |
| 2024 | MultiOrg: A Multi-rater Organoid-detection Dataset. Christina Bukas, Harshavardhan Subramanian, Fenja See, Carina Steinchen, Ivan Ezhov, Gowtham Boosarpu, Sara Asgharpour, Gerald Burgstaller, Mareike Lehmann, Florian Kofler, Marie Piraud |
| 2024 | MultiPull: Detailing Signed Distance Functions by Pulling Multi-Level Queries at Multi-Step. Takeshi Noda, Chao Chen, Weiqi Zhang, Xinhai Liu, Yu-Shen Liu, Zhizhong Han |
| 2024 | MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models. Yichi Zhang, Yao Huang, Yitong Sun, Chang Liu, Zhe Zhao, Zhengwei Fang, Yifan Wang, Huanran Chen, Xiao Yang, Xingxing Wei, Hang Su, Yinpeng Dong, Jun Zhu |
| 2024 | Multiclass Transductive Online Learning. Steve Hanneke, Vinod Raman, Amirreza Shaeiri, Unique Subedi |
| 2024 | Multidimensional Fractional Programming for Normalized Cuts. Yannan Chen, Beichen Huang, Licheng Zhao, Kaiming Shen |
| 2024 | Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization. James Oldfield, Markos Georgopoulos, Grigorios Chrysos, Christos Tzelepis, Yannis Panagakis, Mihalis Nicolaou, Jiankang Deng, Ioannis Patras |
| 2024 | Multilingual Diversity Improves Vision-Language Representations. Thao Nguyen, Matthew Wallingford, Sebastin Santy, Wei-Chiu Ma, Sewoong Oh, Ludwig Schmidt, Pang Wei W. Koh, Ranjay Krishna |
| 2024 | Multimodal Large Language Models Make Text-to-Image Generative Models Align Better. Xun Wu, Shaohan Huang, Guolong Wang, Jing Xiong, Furu Wei |
| 2024 | Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning. Brandon Huang, Chancharik Mitra, Leonid Karlinsky, Assaf Arbelle, Trevor Darrell, Roei Herzig |
| 2024 | Multiple Physics Pretraining for Spatiotemporal Surrogate Models. Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles D. Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Géraud Krawezik, François Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho |
| 2024 | Multistable Shape from Shading Emerges from Patch Diffusion. Xinran Nicole Han, Todd E. Zickler, Ko Nishino |
| 2024 | Multistep Distillation of Diffusion Models via Moment Matching. Tim Salimans, Thomas Mensink, Jonathan Heek, Emiel Hoogeboom |
| 2024 | Multivariate Probabilistic Time Series Forecasting with Correlated Errors. Vincent Zhihao Zheng, Lijun Sun |
| 2024 | Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking. Gabriel Rioux, Apoorva Nitsure, Mattia Rigotti, Kristjan H. Greenewald, Youssef Mroueh |
| 2024 | Multiview Scene Graph. Juexiao Zhang, Gao Zhu, Sihang Li, Xinhao Liu, Haorui Song, Xinran Tang, Chen Feng |
| 2024 | Muscles in Time: Learning to Understand Human Motion In-Depth by Simulating Muscle Activations. David Schneider, Simon Reiß, Marco Kugler, Alexander Jaus, Kunyu Peng, Susanne Sutschet, M. Saquib Sarfraz, Sven Matthiesen, Rainer Stiefelhagen |
| 2024 | MutaPLM: Protein Language Modeling for Mutation Explanation and Engineering. Yizhen Luo, Zikun Nie, Massimo Hong, Suyuan Zhao, Hao Zhou, Zaiqing Nie |
| 2024 | Mutli-Armed Bandits with Network Interference. Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero, Justin Whitehouse |
| 2024 | Mutual Information Estimation via Normalizing Flows. Ivan Butakov, Aleksander Tolmachev, Sofia Malanchuk, Anna Neopryatnaya, Alexey A. Frolov |
| 2024 | Mutual Information Estimation via f-Divergence and Data Derangements. Nunzio Alexandro Letizia, Nicola Novello, Andrea M. Tonello |
| 2024 | N-agent Ad Hoc Teamwork. Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman, Peter Stone |
| 2024 | NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking. Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta |
| 2024 | NN4SysBench: Characterizing Neural Network Verification for Computer Systems. Shuyi Lin, Haoyu He, Tianhao Wei, Kaidi Xu, Huan Zhang, Gagandeep Singh, Changliu Liu, Cheng Tan |
| 2024 | NVRC: Neural Video Representation Compression. Ho Man Kwan, Ge Gao, Fan Zhang, Andrew Gower, David Bull |
| 2024 | NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security. Minghao Shao, Sofija Jancheska, Meet Udeshi, Brendan Dolan-Gavitt, Haoran Xi, Kimberly Milner, Boyuan Chen, Max Yin, Siddharth Garg, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Muhammad Shafique |
| 2024 | NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing. Ting-Hsuan Chen, Jiewen Chan, Hau-Shiang Shiu, Shih-Han Yen, Changhan Yeh, Yu-Lun Liu |
| 2024 | NanoBaseLib: A Multi-Task Benchmark Dataset for Nanopore Sequencing. Guangzhao Cheng, Chengbo Fu, Lu Cheng |
| 2024 | Natural Counterfactuals With Necessary Backtracking. Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang |
| 2024 | NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples. Baiqi Li, Zhiqiu Lin, Wenxuan Peng, Jean de Dieu Nyandwi, Daniel Jiang, Zixian Ma, Simran Khanuja, Ranjay Krishna, Graham Neubig, Deva Ramanan |
| 2024 | Nature-Inspired Local Propagation. Alessandro Betti, Marco Gori |
| 2024 | Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits. Haya Diwan, Jinrui Gou, Cameron Musco, Christopher Musco, Torsten Suel |
| 2024 | Navigating Chemical Space with Latent Flows. Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, Yuanqi Du |
| 2024 | Navigating Extremes: Dynamic Sparsity in Large Output Spaces. Nasibullah Nasibullah, Erik Schultheis, Mike Lasby, Yani Ioannou, Rohit Babbar |
| 2024 | Navigating the Effect of Parametrization for Dimensionality Reduction. Haiyang Huang, Yingfan Wang, Cynthia Rudin |
| 2024 | Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics. Lukas Klein, Carsten T. Lüth, Udo Schlegel, Till J. Bungert, Mennatallah El-Assady, Paul F. Jaeger |
| 2024 | Navigating the Safety Landscape: Measuring Risks in Finetuning Large Language Models. Shengyun Peng, Pin-Yu Chen, Matthew Hull, Duen Horng Chau |
| 2024 | Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. Mark Rowland, Kevin Kevin Li, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney |
| 2024 | Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. Qihao Zhou, Haishan Ye, Luo Luo |
| 2024 | Near-Optimal Distributionally Robust Reinforcement Learning with General $L_p$ Norms. Pierre Clavier, Laixi Shi, Erwan Le Pennec, Eric Mazumdar, Adam Wierman, Matthieu Geist |
| 2024 | Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs. Long-Fei Li, Peng Zhao, Zhi-Hua Zhou |
| 2024 | Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD. Aniket Das, Dheeraj Nagaraj, Soumyabrata Pal, Arun Sai Suggala, Prateek Varshney |
| 2024 | Near-Optimality of Contrastive Divergence Algorithms. Pierre Glaser, Kevin Han Huang, Arthur Gretton |
| 2024 | Nearest Neighbor Speculative Decoding for LLM Generation and Attribution. Minghan Li, Xilun Chen, Ari Holtzman, Beidi Chen, Jimmy Lin, Scott Yih, Victoria Lin |
| 2024 | Nearly Minimax Optimal Regret for Multinomial Logistic Bandit. Joongkyu Lee, Min-hwan Oh |
| 2024 | Nearly Minimax Optimal Submodular Maximization with Bandit Feedback. Artin Tajdini, Lalit Jain, Kevin Jamieson |
| 2024 | Nearly Optimal Approximation of Matrix Functions by the Lanczos Method. Noah Amsel, Tyler Chen, Anne Greenbaum, Cameron Musco, Christopher Musco |
| 2024 | Nearly Tight Black-Box Auditing of Differentially Private Machine Learning. Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro |
| 2024 | Needle In A Multimodal Haystack. Weiyun Wang, Shuibo Zhang, Yiming Ren, Yuchen Duan, Tiantong Li, Shuo Liu, Mengkang Hu, Zhe Chen, Kaipeng Zhang, Lewei Lu, Xizhou Zhu, Ping Luo, Yu Qiao, Jifeng Dai, Wenqi Shao, Wenhai Wang |
| 2024 | Neglected Hessian component explains mysteries in sharpness regularization. Yann N. Dauphin, Atish Agarwala, Hossein Mobahi |
| 2024 | NeoRL: Efficient Exploration for Nonepisodic RL. Bhavya Sukhija, Lenart Treven, Florian Dörfler, Stelian Coros, Andreas Krause |
| 2024 | Nesterov acceleration despite very noisy gradients. Kanan Gupta, Jonathan W. Siegel, Stephan Wojtowytsch |
| 2024 | NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation. Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta, Jing Zhu, Yuxiang Wang |
| 2024 | NeuMA: Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics. Junyi Cao, Shanyan Guan, Yanhao Ge, Wei Li, Xiaokang Yang, Chao Ma |
| 2024 | NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction. Yifan Wang, Di Huang, Weicai Ye, Guofeng Zhang, Wanli Ouyang, Tong He |
| 2024 | Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms. Chengyuan Deng, Jie Gao, Kevin Lu, Feng Luo, Hongbin Sun, Cheng Xin |
| 2024 | Neur2BiLO: Neural Bilevel Optimization. Justin Dumouchelle, Esther Julien, Jannis Kurtz, Elias B. Khalil |
| 2024 | Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models. Ziyi Wu, Yulia Rubanova, Rishabh Kabra, Drew A. Hudson, Igor Gilitschenski, Yusuf Aytar, Sjoerd van Steenkiste, Kelsey R. Allen, Thomas Kipf |
| 2024 | Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks. Wenlin Chen, Hong Ge |
| 2024 | Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization. Zhikang Chen, Min Zhang, Sen Cui, Haoxuan Li, Gang Niu, Mingming Gong, Changshui Zhang, Kun Zhang |
| 2024 | Neural Collapse To Multiple Centers For Imbalanced Data. HongRen Yan, Yuhua Qian, Furong Peng, Jiachen Luo, Zheqing Zhu, Feijiang Li |
| 2024 | Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times. Pei Xiao, Zizhen Zhang, Jinbiao Chen, Jiahai Wang, Zhenzhen Zhang |
| 2024 | Neural Concept Binder. Wolfgang Stammer, Antonia Wüst, David Steinmann, Kristian Kersting |
| 2024 | Neural Conditional Probability for Uncertainty Quantification. Vladimir Kostic, Grégoire Pacreau, Giacomo Turri, Pietro Novelli, Karim Lounici, Massimiliano Pontil |
| 2024 | Neural Cover Selection for Image Steganography. Karl Chahine, Hyeji Kim |
| 2024 | Neural Embeddings Rank: Aligning 3D latent dynamics with movements. Chenggang Chen, Zhiyu Yang, Xiaoqin Wang |
| 2024 | Neural Experts: Mixture of Experts for Implicit Neural Representations. Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Sameera Ramasinghe, Stephen Gould |
| 2024 | Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling. Grigory Bartosh, Dmitry P. Vetrov, Christian Andersson Naesseth |
| 2024 | Neural Gaffer: Relighting Any Object via Diffusion. Haian Jin, Yuan Li, Fujun Luan, Yuanbo Xiangli, Sai Bi, Kai Zhang, Zexiang Xu, Jin Sun, Noah Snavely |
| 2024 | Neural Isometries: Taming Transformations for Equivariant ML. Thomas W. Mitchel, Michael J. Taylor, Vincent Sitzmann |
| 2024 | Neural Krylov Iteration for Accelerating Linear System Solving. Jian Luo, Jie Wang, Hong Wang, Huanshuo Dong, Zijie Geng, Hanzhu Chen, Yufei Kuang |
| 2024 | Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation. István Sárándi, Gerard Pons-Moll |
| 2024 | Neural Model Checking. Mirco Giacobbe, Daniel Kroening, Abhinandan Pal, Michael Tautschnig |
| 2024 | Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations. Nima Dehmamy, Csaba Both, Jeet Mohapatra, Subhro Das, Tommi S. Jaakkola |
| 2024 | Neural P Yusong Wang, Chaoran Cheng, Shaoning Li, Yuxuan Ren, Bin Shao, Ge Liu, Pheng-Ann Heng, Nanning Zheng |
| 2024 | Neural Persistence Dynamics. Sebastian Zeng, Florian Graf, Martin Uray, Stefan Huber, Roland Kwitt |
| 2024 | Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations. Nicholas Gao, Stephan Günnemann |
| 2024 | Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses. Seungwoo Yoo, Juil Koo, Kyeongmin Yeo, Minhyuk Sung |
| 2024 | Neural Residual Diffusion Models for Deep Scalable Vision Generation. Zhiyuan Ma, Liangliang Zhao, Biqing Qi, Bowen Zhou |
| 2024 | Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set. Wenyuan Zhang, Yu-Shen Liu, Zhizhong Han |
| 2024 | Neural collapse vs. low-rank bias: Is deep neural collapse really optimal? Peter Súkeník, Christoph H. Lampert, Marco Mondelli |
| 2024 | Neural decoding from stereotactic EEG: accounting for electrode variability across subjects. Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G. Ramayya, Michelle J. Hedlund, Vivek P. Buch, Kostas Daniilidis, Konrad P. Kording, Flavia Vitale |
| 2024 | Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu |
| 2024 | NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory. Navami Kairanda, Marc Habermann, Christian Theobalt, Vladislav Golyanik |
| 2024 | NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation. Yifei Li, Yuchen Sun, Pingchuan Ma, Eftychios Sifakis, Tao Du, Bo Zhu, Wojciech Matusik |
| 2024 | NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes. Hao-Lun Sun, Lei Hsiung, Nandhini Chandramoorthy, Pin-Yu Chen, Tsung-Yi Ho |
| 2024 | NeuralPlane: An Efficiently Parallelizable Platform for Fixed-wing Aircraft Control with Reinforcement Learning. Chuanyi Xue, Qihan Liu, Xiaoteng Ma, Xinyao Qin, Gui Ning, Yang Qi, Jinsheng Ren, Bin Liang, Jun Yang |
| 2024 | NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks. Bernardo Esteves, Miguel Vasco, Francisco S. Melo |
| 2024 | NeuralSteiner: Learning Steiner Tree for Overflow-avoiding Global Routing in Chip Design. Ruizhi Liu, Zhisheng Zeng, Shizhe Ding, Jingyan Sui, Xingquan Li, Dongbo Bu |
| 2024 | Neuro-Symbolic Data Generation for Math Reasoning. Zenan Li, Zhi Zhou, Yuan Yao, Xian Zhang, Yufeng Li, Chun Cao, Fan Yang, Xiaoxing Ma |
| 2024 | Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction. Guobin Shen, Dongcheng Zhao, Xiang He, Linghao Feng, Yiting Dong, Jihang Wang, Qian Zhang, Yi Zeng |
| 2024 | NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping. Yamin Li, Ange Lou, Ziyuan Xu, Shengchao Zhang, Shiyu Wang, Dario J. Englot, Soheil Kolouri, Daniel Moyer, Roza G. Bayrak, Catie Chang |
| 2024 | NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction. Zixuan Gong, Guangyin Bao, Qi Zhang, Zhongwei Wan, Duoqian Miao, Shoujin Wang, Lei Zhu, Changwei Wang, Rongtao Xu, Liang Hu, Ke Liu, Yu Zhang |
| 2024 | NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation. Chaokang Jiang, Dalong Du, Jiuming Liu, Siting Zhu, Zhenqiang Liu, Zhuang Ma, Zhujin Liang, Jie Zhou |
| 2024 | NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes. Ziquan Wei, Tingting Dan, Jiaqi Ding, Guorong Wu |
| 2024 | Neuronal Competition Groups with Supervised STDP for Spike-Based Classification. Gaspard Goupy, Pierre Tirilly, Ioan Marius Bilasco |
| 2024 | NewTerm: Benchmarking Real-Time New Terms for Large Language Models with Annual Updates. Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Min Zhang, Zhaopeng Tu |
| 2024 | Newswire: A Large-Scale Structured Database of a Century of Historical News. Emily Silcock, Abhishek Arora, Luca D'Amico-Wong, Melissa Dell |
| 2024 | Newton Informed Neural Operator for Solving Nonlinear Partial Differential Equations. Wenrui Hao, Xinliang Liu, Yahong Yang |
| 2024 | Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms. Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne, Oliver Deussen, Stefano Ermon |
| 2024 | Nimbus: Secure and Efficient Two-Party Inference for Transformers. Zhengyi Li, Kang Yang, Jin Tan, Wen-jie Lu, Haoqi Wu, Xiao Wang, Yu Yu, Derun Zhao, Yancheng Zheng, Minyi Guo, Jingwen Leng |
| 2024 | No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance. Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip Torr, Adel Bibi, Samuel Albanie, Matthias Bethge |
| 2024 | No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models. Angéline Pouget, Lucas Beyer, Emanuele Bugliarello, Xiao Wang, Andreas Steiner, Xiaohua Zhai, Ibrahim M. Alabdulmohsin |
| 2024 | No Free Delivery Service: Epistemic limits of passive data collection in complex social systems. Maximilian Nickel |
| 2024 | No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation. Per Kristian Lehre, Shishen Lin |
| 2024 | No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices. Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith |
| 2024 | No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery. Alexander Rutherford, Michael Beukman, Timon Willi, Bruno Lacerda, Nick Hawes, Jakob N. Foerster |
| 2024 | No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO. Skander Moalla, Andrea Miele, Daniil Pyatko, Razvan Pascanu, Caglar Gulcehre |
| 2024 | No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations. Walter Simoncini, Andrei Bursuc, Spyridon Gidaris, Yuki M. Asano |
| 2024 | No-Regret Bandit Exploration based on Soft Tree Ensemble Model. Shogo Iwazaki, Shinya Suzumura |
| 2024 | No-Regret Learning for Fair Multi-Agent Social Welfare Optimization. Mengxiao Zhang, Ramiro Deo-Campo Vuong, Haipeng Luo |
| 2024 | No-Regret M Taihei Oki, Shinsaku Sakaue |
| 2024 | No-regret Learning in Harmonic Games: Extrapolation in the Face of Conflicting Interests. Davide Legacci, Panayotis Mertikopoulos, Christos H. Papadimitriou, Georgios Piliouras, Bary S. R. Pradelski |
| 2024 | NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention. Tianyi Zhang, Jonah Yi, Bowen Yao, Zhaozhuo Xu, Anshumali Shrivastava |
| 2024 | Noether's Razor: Learning Conserved Quantities. Tycho F. A. van der Ouderaa, Mark van der Wilk, Pim de Haan |
| 2024 | Noise Contrastive Alignment of Language Models with Explicit Rewards. Huayu Chen, Guande He, Lifan Yuan, Ganqu Cui, Hang Su, Jun Zhu |
| 2024 | Noise-Aware Differentially Private Regression via Meta-Learning. Ossi Räisä, Stratis Markou, Matthew Ashman, Wessel P. Bruinsma, Marlon Tobaben, Antti Honkela, Richard E. Turner |
| 2024 | NoiseGPT: Label Noise Detection and Rectification through Probability Curvature. Haoyu Wang, Zhuo Huang, Zhiwei Lin, Tongliang Liu |
| 2024 | Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation. Du Chen, Geoffrey A. Chua |
| 2024 | Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom. Tri Nguyen, Shahana Ibrahim, Xiao Fu |
| 2024 | Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods. Jiamian Hu, Yuanyuan Hong, Yihua Chen, He Wang, Moriaki Yasuhara |
| 2024 | NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise. Zhonghao Wang, Danyu Sun, Sheng Zhou, Haobo Wang, Jiapei Fan, Longtao Huang, Jiajun Bu |
| 2024 | Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning. Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus, Felix Krahmer, Holger Rauhut |
| 2024 | Non-Euclidean Mixture Model for Social Network Embedding. Roshni G. Iyer, Yewen Wang, Wei Wang, Yizhou Sun |
| 2024 | Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani |
| 2024 | Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation. Sobihan Surendran, Adeline Fermanian, Antoine Godichon-Baggioni, Sylvain Le Corff |
| 2024 | Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits. Zhan Yu, Qiuhao Chen, Yuling Jiao, Yinan Li, Xiliang Lu, Xin Wang, Jerry Zhijian Yang |
| 2024 | Non-asymptotic Convergence of Training Transformers for Next-token Prediction. Ruiquan Huang, Yingbin Liang, Jing Yang |
| 2024 | Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search. Qiujiang Jin, Ruichen Jiang, Aryan Mokhtari |
| 2024 | Non-convolutional graph neural networks. Yuanqing Wang, Kyunghyun Cho |
| 2024 | Non-geodesically-convex optimization in the Wasserstein space. Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams, Petrus Mikkola, Marcelo Hartmann, Kai Puolamäki, Arto Klami |
| 2024 | Non-parametric classification via expand-and-sparsify representation. Kaushik Sinha |
| 2024 | Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So, Mikael Johansson |
| 2024 | Nonlinear dynamics of localization in neural receptive fields. Leon Lufkin, Andrew M. Saxe, Erin Grant |
| 2024 | Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery. Yue Yu, Ning Liu, Fei Lu, Tian Gao, Siavash Jafarzadeh, Stewart A. Silling |
| 2024 | Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. Zixuan Zhang, Kaiqi Zhang, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang |
| 2024 | Nonparametric Evaluation of Noisy ICA Solutions. Syamantak Kumar, Derek Bean, Peter J. Bickel, Purnamrita Sarkar |
| 2024 | Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients. Yuri R. Fonseca, Caio Peixoto, Yuri F. Saporito |
| 2024 | Nonstationary Sparse Spectral Permanental Process. Zicheng Sun, Yixuan Zhang, Zenan Ling, Xuhui Fan, Feng Zhou |
| 2024 | Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering. Meng Wei, Qianyi Wu, Jianmin Zheng, Hamid Rezatofighi, Jianfei Cai |
| 2024 | Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers. Gavia Gray, Aman Tiwari, Shane Bergsma, Joel Hestness |
| 2024 | Normalization and effective learning rates in reinforcement learning. Clare Lyle, Zeyu Zheng, Khimya Khetarpal, James Martens, Hado Philip van Hasselt, Razvan Pascanu, Will Dabney |
| 2024 | Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features. Benyuan Meng, Qianqian Xu, Zitai Wang, Xiaochun Cao, Qingming Huang |
| 2024 | Not All Tokens Are What You Need for Pretraining. Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu, Yelong Shen, Ruochen Xu, Chen Lin, Yujiu Yang, Jian Jiao, Nan Duan, Weizhu Chen |
| 2024 | Not Just Object, But State: Compositional Incremental Learning without Forgetting. Yanyi Zhang, Binglin Qiu, Qi Jia, Yu Liu, Ran He |
| 2024 | Not so griddy: Internal representations of RNNs path integrating more than one agent. William Redman, Francisco Acosta, Santiago Acosta-Mendoza, Nina Miolane |
| 2024 | Novel Object Synthesis via Adaptive Text-Image Harmony. Zeren Xiong, Zedong Zhang, Zikun Chen, Shuo Chen, Xiang Li, Gan Sun, Jian Yang, Jun Li |
| 2024 | NovoBench: Benchmarking Deep Learning-based \emph{De Novo} Sequencing Methods in Proteomics. Jingbo Zhou, Shaorong Chen, Jun Xia, Sizhe Liu, Tianze Ling, Wenjie Du, Yue Liu, Jianwei Yin, Stan Z. Li |
| 2024 | Nuclear Fusion Diamond Polishing Dataset. Antonios Alexos, Junze Liu, Shashank Galla, Sean Hayes, Kshitij Bhardwaj, Alexander Schwartz, Monika Biener, Pierre Baldi, Satish T. S. Bukkapatnam, Suhas Bhandarkar |
| 2024 | Nuclear Norm Regularization for Deep Learning. Christopher Scarvelis, Justin M. Solomon |
| 2024 | OAM-TCD: A globally diverse dataset of high-resolution tree cover maps. Josh Veitch-Michaelis, Andrew Cottam, Daniella Schweizer, Eben N. Broadbent, David Dao, Ce Zhang, Angelica M. Almeyda Zambrano, Simeon Max |
| 2024 | OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning. Yihang Yao, Zhepeng Cen, Wenhao Ding, Haohong Lin, Shiqi Liu, Tingnan Zhang, Wenhao Yu, Ding Zhao |
| 2024 | ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models. Jingyuan Zhu, Shiyu Li, Yuxuan Liu, Jian Yuan, Ping Huang, Jiulong Shan, Huimin Ma |
| 2024 | ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings. Suyoung Lee, Jaeyoung Chung, Jaeyoo Huh, Kyoung Mu Lee |
| 2024 | ODRL: A Benchmark for Off-Dynamics Reinforcement Learning. Jiafei Lyu, Kang Xu, Jiacheng Xu, Mengbei Yan, Jingwen Yang, Zongzhang Zhang, Chenjia Bai, Zongqing Lu, Xiu Li |
| 2024 | OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding. Tao Zhang, Xiangtai Li, Hao Fei, Haobo Yuan, Shengqiong Wu, Shunping Ji, Chen Change Loy, Shuicheng Yan |
| 2024 | OPEL: Optimal Transport Guided ProcedurE Learning. Sayeed Shafayet Chowdhury, Soumyadeep Chandra, Kaushik Roy |
| 2024 | OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators. Allen Nie, Yash Chandak, Christina J. Yuan, Anirudhan Badrinath, Yannis Flet-Berliac, Emma Brunskill |
| 2024 | OPUS: Occupancy Prediction Using a Sparse Set. Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, Ming-Ming Cheng |
| 2024 | OSLO: One-Shot Label-Only Membership Inference Attacks. Yuefeng Peng, Jaechul Roh, Subhransu Maji, Amir Houmansadr |
| 2024 | OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments. Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, Tao Yu |
| 2024 | OT4P: Unlocking Effective Orthogonal Group Path for Permutation Relaxation. Yaming Guo, Chen Zhu, Hengshu Zhu, Tieru Wu |
| 2024 | OTTER: Effortless Label Distribution Adaptation of Zero-shot Models. Changho Shin, Jitian Zhao, Sonia Cromp, Harit Vishwakarma, Frederic Sala |
| 2024 | OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking. Haiji Liang, Ruize Han |
| 2024 | OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning. Anwesa Choudhuri, Girish Chowdhary, Alexander G. Schwing |
| 2024 | Object segmentation from common fate: Motion energy processing enables human-like zero-shot generalization to random dot stimuli. Matthias Tangemann, Matthias Kümmerer, Matthias Bethge |
| 2024 | Observational Scaling Laws and the Predictability of Langauge Model Performance. Yangjun Ruan, Chris J. Maddison, Tatsunori B. Hashimoto |
| 2024 | OccFusion: Rendering Occluded Humans with Generative Diffusion Priors. Adam Sun, Tiange Xiang, Scott L. Delp, Li Fei-Fei, Ehsan Adeli |
| 2024 | OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step. Owen Dugan, Donato Jiménez-Benetó, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljacic |
| 2024 | Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality. Audrey Huang, Nan Jiang |
| 2024 | Octopus: A Multi-modal LLM with Parallel Recognition and Sequential Understanding. Chuyang Zhao, Yuxin Song, Junru Chen, Kang Rong, Haocheng Feng, Gang Zhang, Shufan Ji, Jingdong Wang, Errui Ding, Yifan Sun |
| 2024 | OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries. Yuhang Lu, Xinge Zhu, Tai Wang, Yuexin Ma |
| 2024 | Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting. Brandon Victor, Mathilde Letard, Peter Naylor, Karim Douch, Nicolas Longépé, Zhen He, Patrick Ebel |
| 2024 | Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation. Yihong Guo, Yixuan Wang, Yuanyuan Shi, Pan Xu, Anqi Liu |
| 2024 | Off-Policy Selection for Initiating Human-Centric Experimental Design. Ge Gao, Xi Yang, Qitong Gao, Song Ju, Miroslav Pajic, Min Chi |
| 2024 | Off-policy estimation with adaptively collected data: the power of online learning. Jeonghwan Lee, Cong Ma |
| 2024 | Offline Behavior Distillation. Shiye Lei, Sen Zhang, Dacheng Tao |
| 2024 | Offline Multitask Representation Learning for Reinforcement Learning. Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup |
| 2024 | Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff. Jian Qian, Haichen Hu, David Simchi-Levi |
| 2024 | Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression. Yixiu Mao, Qi Wang, Chen Chen, Yun Qu, Xiangyang Ji |
| 2024 | Oja's Algorithm for Streaming Sparse PCA. Syamantak Kumar, Purnamrita Sarkar |
| 2024 | OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI. Zhen Huang, Zengzhi Wang, Shijie Xia, Xuefeng Li, Haoyang Zou, Ruijie Xu, Run-Ze Fan, Lyumanshan Ye, Ethan Chern, Yixin Ye, Yikai Zhang, Yuqing Yang, Ting Wu, Binjie Wang, Shichao Sun, Yang Xiao, Yiyuan Li, Fan Zhou, Steffi Chern, Yiwei Qin, Yan Ma, Jiadi Su, Yixiu Liu, Yuxiang Zheng, Shaoting Zhang, Dahua Lin, Yu Qiao, Pengfei Liu |
| 2024 | OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents. Zihao Wang, Shaofei Cai, Zhancun Mu, Haowei Lin, Ceyao Zhang, Xuejie Liu, Qing Li, Anji Liu, Xiaojian (Shawn) Ma, Yitao Liang |
| 2024 | OmniTokenizer: A Joint Image-Video Tokenizer for Visual Generation. Junke Wang, Yi Jiang, Zehuan Yuan, Bingyue Peng, Zuxuan Wu, Yu-Gang Jiang |
| 2024 | Omnigrasp: Grasping Diverse Objects with Simulated Humanoids. Zhengyi Luo, Jinkun Cao, Sammy Christen, Alexander Winkler, Kris Kitani, Weipeng Xu |
| 2024 | On $f$-Divergence Principled Domain Adaptation: An Improved Framework. Ziqiao Wang, Yongyi Mao |
| 2024 | On Affine Homotopy between Language Encoders. Robin Chan, Reda Boumasmoud, Anej Svete, Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel, Mrinmaya Sachan, Bernhard Schölkopf, Mennatallah El-Assady, Ryan Cotterell |
| 2024 | On Causal Discovery in the Presence of Deterministic Relations. Loka Li, Haoyue Dai, Hanin Al Ghothani, Biwei Huang, Jiji Zhang, Shahar Harel, Isaac Bentwich, Guangyi Chen, Kun Zhang |
| 2024 | On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions. Yusu Hong, Junhong Lin |
| 2024 | On Differentially Private Subspace Estimation in a Distribution-Free Setting. Eliad Tsfadia |
| 2024 | On Differentially Private U Statistics. Kamalika Chaudhuri, Po-Ling Loh, Shourya Pandey, Purnamrita Sarkar |
| 2024 | On Divergence Measures for Training GFlowNets. Tiago da Silva, Eliezer de Souza da Silva, Diego Mesquita |
| 2024 | On Feature Learning in Structured State Space Models. Leena Chennuru Vankadara, Jin Xu, Moritz Haas, Volkan Cevher |
| 2024 | On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion. Chenghao Fan, Zhenyi Lu, Wei Wei, Jie Tian, Xiaoye Qu, Dangyang Chen, Yu Cheng |
| 2024 | On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection. Xiufeng Song, Xiao Guo, Jiache Zhang, Qirui Li, Lei Bai, Xiaoming Liu, Guangtao Zhai, Xiaohong Liu |
| 2024 | On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability. Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li |
| 2024 | On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. Boyao Li, Alexander Thomson, Houssam Nassif, Matthew Engelhard, David Page |
| 2024 | On Sampling Strategies for Spectral Model Sharding. Denis Korzhenkov, Christos Louizos |
| 2024 | On Socially Fair Low-Rank Approximation and Column Subset Selection. Zhao Song, Ali Vakilian, David P. Woodruff, Samson Zhou |
| 2024 | On Softmax Direct Preference Optimization for Recommendation. Yuxin Chen, Junfei Tan, An Zhang, Zhengyi Yang, Leheng Sheng, Enzhi Zhang, Xiang Wang, Tat-Seng Chua |
| 2024 | On Sparse Canonical Correlation Analysis. Yongchun Li, Santanu Dey, Weijun Xie |
| 2024 | On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs). Jerry Yao-Chieh Hu, Weimin Wu, Zhuoru Li, Sophia Pi, Zhao Song, Han Liu |
| 2024 | On Tractable Φ-Equilibria in Non-Concave Games. Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng |
| 2024 | On Weak Regret Analysis for Dueling Bandits. El Mehdi Saad, Alexandra Carpentier, Tomás Kocák, Nicolas Verzelen |
| 2024 | On conditional diffusion models for PDE simulations. Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu |
| 2024 | On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models. Tariq Berrada Ifriqi, Pietro Astolfi, Melissa Hall, Reyhane Askari Hemmat, Yohann Benchetrit, Marton Havasi, Matthew J. Muckley, Karteek Alahari, Adriana Romero-Soriano, Jakob Verbeek, Michal Drozdzal |
| 2024 | On provable privacy vulnerabilities of graph representations. Ruofan Wu, Guanhua Fang, Mingyang Zhang, Qiying Pan, Tengfei Liu, Weiqiang Wang |
| 2024 | On scalable oversight with weak LLMs judging strong LLMs. Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah |
| 2024 | On the Ability of Developers' Training Data Preservation of Learnware. Hao-Yi Lei, Zhi-Hao Tan, Zhi-Hua Zhou |
| 2024 | On the Adversarial Robustness of Benjamini Hochberg. Louis Chen, Roberto Szechtman, Matan Seri |
| 2024 | On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift. Pratiksha Thaker, Amrith Setlur, Steven Z. Wu, Virginia Smith |
| 2024 | On the Comparison between Multi-modal and Single-modal Contrastive Learning. Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao, Zhiqiang Xu, Taiji Suzuki |
| 2024 | On the Complexity of Identification in Linear Structural Causal Models. Julian Dörfler, Benito van der Zander, Markus Bläser, Maciej Liskiewicz |
| 2024 | On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. Nirmit Joshi, Theodor Misiakiewicz, Nati Srebro |
| 2024 | On the Complexity of Teaching a Family of Linear Behavior Cloning Learners. Shubham Kumar Bharti, Stephen Wright, Adish Singla, Xiaojin (Jerry) Zhu |
| 2024 | On the Computational Complexity of Private High-dimensional Model Selection. Saptarshi Roy, Zehua Wang, Ambuj Tewari |
| 2024 | On the Computational Landscape of Replicable Learning. Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas, Felix Zhou |
| 2024 | On the Convergence of Loss and Uncertainty-based Active Learning Algorithms. Daniel Haimovich, Dima Karamshuk, Fridolin Linder, Niek Tax, Milan Vojnovic |
| 2024 | On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation. Yuheng Zhang, Nan Jiang |
| 2024 | On the Effects of Data Scale on UI Control Agents. Wei Li, William E. Bishop, Alice Li, Christopher Rawles, Folawiyo Campbell-Ajala, Divya Tyamagundlu, Oriana Riva |
| 2024 | On the Efficiency of ERM in Feature Learning. Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu |
| 2024 | On the Expressive Power of Tree-Structured Probabilistic Circuits. Lang Yin, Han Zhao |
| 2024 | On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks. Paolo Pellizzoni, Till Hendrik Schulz, Dexiong Chen, Karsten M. Borgwardt |
| 2024 | On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution. Yubo Ye, Maryam Toloubidokhti, Sumeet Vadhavkar, Xiajun Jiang, Huafeng Liu, Linwei Wang |
| 2024 | On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function. Yu Xiang, Jie Qiao, Zefeng Liang, Zihuai Zeng, Ruichu Cai, Zhifeng Hao |
| 2024 | On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks. Jiong Zhu, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, Danai Koutra |
| 2024 | On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory. Guhan Chen, Yicheng Li, Qian Lin |
| 2024 | On the Inductive Bias of Stacking Towards Improving Reasoning. Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank Jakkam Reddi, Sanjiv Kumar |
| 2024 | On the Limitations of Fractal Dimension as a Measure of Generalization. Charlie Tan, Inés García-Redondo, Qiquan Wang, Michael M. Bronstein, Anthea Monod |
| 2024 | On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice. Shinji Ito |
| 2024 | On the Necessity of Collaboration for Online Model Selection with Decentralized Data. Junfan Li, Zheshun Wu, Zenglin Xu, Irwin King |
| 2024 | On the Noise Robustness of In-Context Learning for Text Generation. Hongfu Gao, Feipeng Zhang, Wenyu Jiang, Jun Shu, Feng Zheng, Hongxin Wei |
| 2024 | On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization. Alexander Tyurin, Peter Richtárik |
| 2024 | On the Optimality of Dilated Entropy and Lower Bounds for Online Learning in Extensive-Form Games. Zhiyuan Fan, Christian Kroer, Gabriele Farina |
| 2024 | On the Parameter Identifiability of Partially Observed Linear Causal Models. Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang |
| 2024 | On the Power of Decision Trees in Auto-Regressive Language Modeling. Yulu Gan, Tomer Galanti, Tomaso A. Poggio, Eran Malach |
| 2024 | On the Power of Small-size Graph Neural Networks for Linear Programming. Qian Li, Tian Ding, Linxin Yang, Minghui Ouyang, Qingjiang Shi, Ruoyu Sun |
| 2024 | On the Robustness of Spectral Algorithms for Semirandom Stochastic Block Models. Aditya Bhaskara, Agastya Vibhuti Jha, Michael Kapralov, Naren Manoj, Davide Mazzali, Weronika Wrzos-Kaminska |
| 2024 | On the Role of Attention Masks and LayerNorm in Transformers. Xinyi Wu, Amir Ajorlou, Yifei Wang, Stefanie Jegelka, Ali Jadbabaie |
| 2024 | On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games. Awni Altabaa, Zhuoran Yang |
| 2024 | On the Saturation Effects of Spectral Algorithms in Large Dimensions. Weihao Lu, Haobo Zhang, Yicheng Li, Qian Lin |
| 2024 | On the Scalability of Certified Adversarial Robustness with Generated Data. Thomas Altstidl, David Dobre, Arthur Kosmala, Bjoern M. Eskofier, Gauthier Gidel, Leo Schwinn |
| 2024 | On the Scalability of GNNs for Molecular Graphs. Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei, Nia Dickson, Karush Suri, Philip Fradkin, Dominique Beaini |
| 2024 | On the Sparsity of the Strong Lottery Ticket Hypothesis. Emanuele Natale, Davide Ferré, Giordano Giambartolomei, Frédéric Giroire, Frederik Mallmann-Trenn |
| 2024 | On the Stability and Generalization of Meta-Learning. Yunjuan Wang, Raman Arora |
| 2024 | On the Surprising Effectiveness of Attention Transfer for Vision Transformers. Alexander C. Li, Yuandong Tian, Beidi Chen, Deepak Pathak, Xinlei Chen |
| 2024 | On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity. Chao Wang, Xin He, Yuwen Wang, Junhui Wang |
| 2024 | On the Use of Anchoring for Training Vision Models. Vivek Sivaraman Narayanaswamy, Kowshik Thopalli, Rushil Anirudh, Yamen Mubarka, Wesam A. Sakla, Jayaraman J. Thiagarajan |
| 2024 | On the Worst Prompt Performance of Large Language Models. Bowen Cao, Deng Cai, Zhisong Zhang, Yuexian Zou, Wai Lam |
| 2024 | On the cohesion and separability of average-link for hierarchical agglomerative clustering. Eduardo Laber, Miguel Batista |
| 2024 | On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance. Zhixiong Nan, Yilong Chen, Tianfei Zhou, Tao Xiang |
| 2024 | Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge. Fang Dong, Mengyi Chen, Jixian Zhou, Yubin Shi, Yixuan Chen, Mingzhi Dong, Yujiang Wang, Dongsheng Li, Xiaochen Yang, Rui Zhu, Robert P. Dick, Qin Lv, Fan Yang, Tun Lu, Ning Gu, Li Shang |
| 2024 | One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently. Weida Li, Yaoliang Yu |
| 2024 | One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos. Zechen Bai, Tong He, Haiyang Mei, Pichao Wang, Ziteng Gao, Joya Chen, Lei Liu, Zheng Zhang, Mike Zheng Shou |
| 2024 | One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection. Zhenyu Wang, Yali Li, Hengshuang Zhao, Shengjin Wang |
| 2024 | One-Layer Transformer Provably Learns One-Nearest Neighbor In Context. Zihao Li, Yuan Cao, Cheng Gao, Yihan He, Han Liu, Jason M. Klusowski, Jianqing Fan, Mengdi Wang |
| 2024 | One-Shot Safety Alignment for Large Language Models via Optimal Dualization. Xinmeng Huang, Shuo Li, Edgar Dobriban, Osbert Bastani, Hamed Hassani, Dongsheng Ding |
| 2024 | One-Step Diffusion Distillation through Score Implicit Matching. Weijian Luo, Zemin Huang, Zhengyang Geng, J. Zico Kolter, Guo-Jun Qi |
| 2024 | One-Step Effective Diffusion Network for Real-World Image Super-Resolution. Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, Lei Zhang |
| 2024 | One-shot Federated Learning via Synthetic Distiller-Distillate Communication. Junyuan Zhang, Songhua Liu, Xinchao Wang |
| 2024 | One-to-Multiple: A Progressive Style Transfer Unsupervised Domain-Adaptive Framework for Kidney Tumor Segmentation. Kai Hu, Jinhao Li, Yuan Zhang, Xiongjun Ye, Xieping Gao |
| 2024 | One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection. Yiyue Li, Shaoting Zhang, Kang Li, Qicheng Lao |
| 2024 | OneActor: Consistent Subject Generation via Cluster-Conditioned Guidance. Jiahao Wang, Caixia Yan, Haonan Lin, Weizhan Zhang, Mengmeng Wang, Tieliang Gong, Guang Dai, Hao Sun |
| 2024 | OneBit: Towards Extremely Low-bit Large Language Models. Yuzhuang Xu, Xu Han, Zonghan Yang, Shuo Wang, Qingfu Zhu, Zhiyuan Liu, Weidong Liu, Wanxiang Che |
| 2024 | OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring Modeling. Linhui Xiao, Xiaoshan Yang, Fang Peng, Yaowei Wang, Changsheng Xu |
| 2024 | Online Adaptation of Language Models with a Memory of Amortized Contexts. Jihoon Tack, Jaehyung Kim, Eric Mitchell, Jinwoo Shin, Yee Whye Teh, Jonathan Richard Schwarz |
| 2024 | Online Bayesian Persuasion Without a Clue. Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti |
| 2024 | Online Budgeted Matching with General Bids. Jianyi Yang, Pengfei Li, Adam Wierman, Shaolei Ren |
| 2024 | Online Classification with Predictions. Vinod Raman, Ambuj Tewari |
| 2024 | Online Composite Optimization Between Stochastic and Adversarial Environments. Yibo Wang, Sijia Chen, Wei Jiang, Wenhao Yang, Yuanyu Wan, Lijun Zhang |
| 2024 | Online Consistency of the Nearest Neighbor Rule. Geelon So, Sanjoy Dasgupta |
| 2024 | Online Control in Population Dynamics. Noah Golowich, Elad Hazan, Zhou Lu, Dhruv Rohatgi, Y. Jennifer Sun |
| 2024 | Online Control with Adversarial Disturbance for Continuous-time Linear Systems. Jingwei Li, Jing Dong, Can Chang, Baoxiang Wang, Jingzhao Zhang |
| 2024 | Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously. Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster |
| 2024 | Online Estimation via Offline Estimation: An Information-Theoretic Framework. Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin |
| 2024 | Online Feature Updates Improve Online (Generalized) Label Shift Adaptation. Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q. Weinberger |
| 2024 | Online Iterative Reinforcement Learning from Human Feedback with General Preference Model. Chenlu Ye, Wei Xiong, Yuheng Zhang, Hanze Dong, Nan Jiang, Tong Zhang |
| 2024 | Online Learning of Delayed Choices. Recep Yusuf Bekci |
| 2024 | Online Learning with Sublinear Best-Action Queries. Matteo Russo, Andrea Celli, Riccardo Colini-Baldeschi, Federico Fusco, Daniel Haimovich, Dima Karamshuk, Stefano Leonardi, Niek Tax |
| 2024 | Online Non-convex Learning in Dynamic Environments. Zhipan Xu, Lijun Zhang |
| 2024 | Online Posterior Sampling with a Diffusion Prior. Branislav Kveton, Boris Oreshkin, Youngsuk Park, Aniket Deshmukh, Rui Song |
| 2024 | Online Relational Inference for Evolving Multi-agent Interacting Systems. Beomseok Kang, Priyabrata Saha, Sudarshan Sharma, Biswadeep Chakraborty, Saibal Mukhopadhyay |
| 2024 | Online Weighted Paging with Unknown Weights. Orin Levy, Noam Touitou, Aviv Rosenberg |
| 2024 | OnlineTAS: An Online Baseline for Temporal Action Segmentation. Qing Zhong, Guodong Ding, Angela Yao |
| 2024 | Only Strict Saddles in the Energy Landscape of Predictive Coding Networks? Francesco Innocenti, El Mehdi Achour, Ryan Singh, Christopher L. Buckley |
| 2024 | Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives. Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola E. Olatunji, Michael Backes, Adam Dziedzic |
| 2024 | Open-Book Neural Algorithmic Reasoning. Hefei Li, Chao Peng, Chenyang Xu, Zhengfeng Yang |
| 2024 | Open-Vocabulary Object Detection via Language Hierarchy. Jiaxing Huang, Jingyi Zhang, Kai Jiang, Shijian Lu |
| 2024 | OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset. Allen Roush, Yusuf Shabazz, Arvind Balaji, Peter Zhang, Stefano Mezza, Markus Zhang, Sanjay Basu, Sriram Vishwanath, Ravid Shwartz-Ziv |
| 2024 | OpenDlign: Open-World Point Cloud Understanding with Depth-Aligned Images. Ye Mao, Junpeng Jing, Krystian Mikolajczyk |
| 2024 | OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding. Yanmin Wu, Jiarui Meng, Haijie Li, Chenming Wu, Yahao Shi, Xinhua Cheng, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, Jian Zhang |
| 2024 | OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset. Shubham Toshniwal, Ivan Moshkov, Sean Narenthiran, Daria Gitman, Fei Jia, Igor Gitman |
| 2024 | OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction. Hongbo Zhao, Lue Fan, Yuntao Chen, Haochen Wang, Yuran Yang, Xiaojuan Jin, Yixin Zhang, Gaofeng Meng, Zhao-Xiang Zhang |
| 2024 | Operator World Models for Reinforcement Learning. Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto |
| 2024 | Opponent Modeling based on Subgoal Inference. Xiaopeng Yu, Jiechuan Jiang, Zongqing Lu |
| 2024 | Opponent Modeling with In-context Search. Yuheng Jing, Bingyun Liu, Kai Li, Yifan Zang, Haobo Fu, Qiang Fu, Junliang Xing, Jian Cheng |
| 2024 | OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations. Yao Shu, Jiongfeng Fang, Ying He, Fei Richard Yu |
| 2024 | Optical Diffusion Models for Image Generation. Ilker Oguz, Niyazi Ulas Dinç, Mustafa Yildirim, Junjie Ke, Innfarn Yoo, Qifei Wang, Feng Yang, Christophe Moser, Demetri Psaltis |
| 2024 | Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift. Jiawei Ge, Debarghya Mukherjee, Jianqing Fan |
| 2024 | Optimal Algorithms for Augmented Testing of Discrete Distributions. Maryam Aliakbarpour, Piotr Indyk, Ronitt Rubinfeld, Sandeep Silwal |
| 2024 | Optimal Algorithms for Learning Partitions with Faulty Oracles. Adela Frances DePavia, Olga Medrano Martín del Campo, Erasmo Tani |
| 2024 | Optimal Algorithms for Online Convex Optimization with Adversarial Constraints. Abhishek Sinha, Rahul Vaze |
| 2024 | Optimal Batched Best Arm Identification. Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu |
| 2024 | Optimal Classification under Performative Distribution Shift. Edwige Cyffers, Muni Sreenivas Pydi, Jamal Atif, Olivier Cappé |
| 2024 | Optimal Design for Human Preference Elicitation. Subhojyoti Mukherjee, Anusha Lalitha, Kousha Kalantari, Aniket Deshmukh, Ge Liu, Yifei Ma, Branislav Kveton |
| 2024 | Optimal Flow Matching: Learning Straight Trajectories in Just One Step. Nikita Kornilov, Petr Mokrov, Alexander V. Gasnikov, Alexander Korotin |
| 2024 | Optimal Hypothesis Selection in (Almost) Linear Time. Maryam Aliakbarpour, Mark Bun, Adam Smith |
| 2024 | Optimal Multi-Fidelity Best-Arm Identification. Riccardo Poiani, Rémy Degenne, Emilie Kaufmann, Alberto Maria Metelli, Marcello Restelli |
| 2024 | Optimal Multiclass U-Calibration Error and Beyond. Haipeng Luo, Spandan Senapati, Vatsal Sharan |
| 2024 | Optimal Parallelization of Boosting. Arthur da Cunha, Mikael Møller Høgsgaard, Kasper Green Larsen |
| 2024 | Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime. Lasse Vuursteen |
| 2024 | Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li |
| 2024 | Optimal Scalarizations for Sublinear Hypervolume Regret. Qiuyi (Richard) Zhang |
| 2024 | Optimal Top-Two Method for Best Arm Identification and Fluid Analysis. Agniv Bandyopadhyay, Sandeep Juneja, Shubhada Agrawal |
| 2024 | Optimal Transport-based Labor-free Text Prompt Modeling for Sketch Re-identification. Rui Li, TingTing Ren, Jie Wen, Jinxing Li |
| 2024 | Optimal ablation for interpretability. Maximilian Li, Lucas Janson |
| 2024 | Optimal and Approximate Adaptive Stochastic Quantization. Ran Ben-Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher, Shay Vargaftik |
| 2024 | Optimal deep learning of holomorphic operators between Banach spaces. Ben Adcock, Nick C. Dexter, Sebastian Moraga Scheuermann |
| 2024 | Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos. Cuong Le, John Viktor Johansson, Manon Kok, Bastian Wandt |
| 2024 | Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL. Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang, Sheng-Jun Huang |
| 2024 | Optimistic Verifiable Training by Controlling Hardware Nondeterminism. Megha Srivastava, Simran Arora, Dan Boneh |
| 2024 | Optimization Algorithm Design via Electric Circuits. Stephen Boyd, Tetiana Parshakova, Ernest K. Ryu, Jaewook J. Suh |
| 2024 | Optimization Can Learn Johnson Lindenstrauss Embeddings. Nikos Tsikouras, Constantine Caramanis, Christos Tzamos |
| 2024 | Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning. Jaehyun Nam, Kyuyoung Kim, Seunghyuk Oh, Jihoon Tack, Jaehyung Kim, Jinwoo Shin |
| 2024 | Optimizing Automatic Differentiation with Deep Reinforcement Learning. Jamie Lohoff, Emre Neftci |
| 2024 | Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition. Shihong Ding, Long Yang, Luo Luo, Cong Fang |
| 2024 | Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits. Dorian Baudry, Hugo Richard, Maria Cherifa, Vianney Perchet, Clément Calauzènes |
| 2024 | Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks. Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen, Dongmei Jiang, Liqiang Nie |
| 2024 | Oracle-Efficient Differentially Private Learning with Public Data. Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu |
| 2024 | Oracle-Efficient Reinforcement Learning for Max Value Ensembles. Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell |
| 2024 | Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling. Mahdi Karami, Ali Ghodsi |
| 2024 | Order-Independence Without Fine Tuning. Reid McIlroy-Young, Katrina Brown, Conlan Olson, Linjun Zhang, Cynthia Dwork |
| 2024 | Ordered Momentum for Asynchronous SGD. Chang-Wei Shi, Yi-Rui Yang, Wu-Jun Li |
| 2024 | Ordering-Based Causal Discovery for Linear and Nonlinear Relations. Zhuopeng Xu, Yujie Li, Cheng Liu, Ning Gui |
| 2024 | Out-Of-Distribution Detection with Diversification (Provably). Haiyun Yao, Zongbo Han, Huazhu Fu, Xi Peng, Qinghua Hu, Changqing Zhang |
| 2024 | Out-of-Distribution Detection with a Single Unconditional Diffusion Model. Alvin Heng, Alexandre H. Thiery, Harold Soh |
| 2024 | Outlier-Robust Distributionally Robust Optimization via Unbalanced Optimal Transport. Zifan Wang, Yi Shen, Michael M. Zavlanos, Karl Henrik Johansson |
| 2024 | Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation. Yu-Liang Zhan, Zhong-Yi Lu, Hao Sun, Ze-Feng Gao |
| 2024 | Overcoming Brittleness in Pareto-Optimal Learning Augmented Algorithms. Alex Elenter, Spyros Angelopoulos, Christoph Dürr, Yanni Lefki |
| 2024 | Overcoming Common Flaws in the Evaluation of Selective Classification Systems. Jeremias Traub, Till J. Bungert, Carsten T. Lüth, Michael Baumgartner, Klaus H. Maier-Hein, Lena Maier-Hein, Paul F. Jaeger |
| 2024 | Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL. Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Kevin Jamieson, Abhishek Gupta |
| 2024 | Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality. Marko Medvedev, Gal Vardi, Nati Srebro |
| 2024 | OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. Shengjie Niu, Lifan Lin, Jian Huang, Chao Wang |
| 2024 | OxonFair: A Flexible Toolkit for Algorithmic Fairness. Eoin Delaney, Zihao Fu, Sandra Wachter, Brent D. Mittelstadt, Chris Russell |
| 2024 | P Qi Wang, Pu Ren, Hao Zhou, Xin-Yang Liu, Zhiwen Deng, Yi Zhang, Zeruizhi Cheng, Hongsheng Liu, Zidong Wang, Jian-Xun Wang, Ji-Rong Wen, Hao Sun, Yang Liu |
| 2024 | PAC-Bayes-Chernoff bounds for unbounded losses. Ioar Casado, Luis A. Ortega Andrés, Aritz Pérez, Andrés R. Masegosa |
| 2024 | PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization. Yao Ni, Shan Zhang, Piotr Koniusz |
| 2024 | PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices. Hanqing Zhu, Wenyan Cong, Guojin Chen, Shupeng Ning, Ray T. Chen, Jiaqi Gu, David Z. Pan |
| 2024 | PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining. Mishaal Kazmi, Hadrien Lautraite, Alireza Akbari, Qiaoyue Tang, Mauricio Soroco, Tao Wang, Sébastien Gambs, Mathias Lécuyer |
| 2024 | PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders. Xiangdong Zhang, Shaofeng Zhang, Junchi Yan |
| 2024 | PCoTTA: Continual Test-Time Adaptation for Multi-Task Point Cloud Understanding. Jincen Jiang, Qianyu Zhou, Yuhang Li, Xinkui Zhao, Meili Wang, Lizhuang Ma, Jian Chang, Jian Jun Zhang, Xuequan Lu |
| 2024 | PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning. Chengyang Ying, Zhongkai Hao, Xinning Zhou, Xuezhou Xu, Hang Su, Xingxing Zhang, Jun Zhu |
| 2024 | PEACE: A Dataset of Pharmaceutical Care for Cancer Pain Analgesia Evaluation and Medication Decision. Yutao Dou, Huimin Yu, Wei Li, Jingyang Li, Fei Xia, Jian Xiao |
| 2024 | PERIA: Perceive, Reason, Imagine, Act via Holistic Language and Vision Planning for Manipulation. Fei Ni, Jianye Hao, Shiguang Wu, Longxin Kou, Yifu Yuan, Zibin Dong, Jinyi Liu, Mingzhi Li, Yuzheng Zhuang, Yan Zheng |
| 2024 | PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting. Yuxin Jia, Youfang Lin, Jing Yu, Shuo Wang, Tianhao Liu, Huaiyu Wan |
| 2024 | PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs. Zhongkai Hao, Jiachen Yao, Chang Su, Hang Su, Ziao Wang, Fanzhi Lu, Zeyu Xia, Yichi Zhang, Songming Liu, Lu Lu, Jun Zhu |
| 2024 | PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation. Kaidong Zhang, Pengzhen Ren, Bingqian Lin, Junfan Lin, Shikui Ma, Hang Xu, Xiaodan Liang |
| 2024 | PLIP: Language-Image Pre-training for Person Representation Learning. Jialong Zuo, Jiahao Hong, Feng Zhang, Changqian Yu, Hanyu Zhou, Changxin Gao, Nong Sang, Jingdong Wang |
| 2024 | PPLNs: Parametric Piecewise Linear Networks for Event-Based Temporal Modeling and Beyond. Chen Song, Zhenxiao Liang, Bo Sun, Qixing Huang |
| 2024 | PRODuctive bandits: Importance Weighting No More. Julian Zimmert, Teodor Vanislavov Marinov |
| 2024 | PROSPECT PTMs: Rich Labeled Tandem Mass Spectrometry Dataset of Modified Peptides for Machine Learning in Proteomics. Wassim Gabriel, Omar Shouman, Eva Ayla Schröder, Florian Bößl, Mathias Wilhelm |
| 2024 | PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation. Weiqin Yang, Jiawei Chen, Xin Xin, Sheng Zhou, Binbin Hu, Yan Feng, Chun Chen, Can Wang |
| 2024 | PTQ4DiT: Post-training Quantization for Diffusion Transformers. Junyi Wu, Haoxuan Wang, Yuzhang Shang, Mubarak Shah, Yan Yan |
| 2024 | PURE: Prompt Evolution with Graph ODE for Out-of-distribution Fluid Dynamics Modeling. Hao Wu, Changhu Wang, Fan Xu, Jinbao Xue, Chong Chen, Xian-Sheng Hua, Xiao Luo |
| 2024 | PUZZLES: A Benchmark for Neural Algorithmic Reasoning. Benjamin Estermann, Luca A. Lanzendörfer, Yannick Niedermayr, Roger Wattenhofer |
| 2024 | PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik |
| 2024 | PaCE: Parsimonious Concept Engineering for Large Language Models. Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan, Darshan Thaker, Aditya Chattopadhyay, Chris Callison-Burch, René Vidal |
| 2024 | PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition. Jinghui Lu, Yanjie Wang, Ziwei Yang, Xuejing Liu, Brian Mac Namee, Can Huang |
| 2024 | PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher. Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon |
| 2024 | PageRank Bandits for Link Prediction. Yikun Ban, Jiaru Zou, Zihao Li, Yunzhe Qi, Dongqi Fu, Jian Kang, Hanghang Tong, Jingrui He |
| 2024 | Paloma: A Benchmark for Evaluating Language Model Fit. Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hanna Hajishirzi, Noah A. Smith, Kyle Richardson, Jesse Dodge |
| 2024 | Panacea: Pareto Alignment via Preference Adaptation for LLMs. Yifan Zhong, Chengdong Ma, Xiaoyuan Zhang, Ziran Yang, Haojun Chen, Qingfu Zhang, Siyuan Qi, Yaodong Yang |
| 2024 | Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models. Daizong Liu, Mingyu Yang, Xiaoye Qu, Pan Zhou, Xiang Fang, Keke Tang, Yao Wan, Lichao Sun |
| 2024 | Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation. Heeseung Kim, Soonshin Seo, Kyeongseok Jeong, Ohsung Kwon, Soyoon Kim, Jungwhan Kim, Jaehong Lee, Eunwoo Song, Myungwoo Oh, Jung-Woo Ha, Sungroh Yoon, Kang Min Yoo |
| 2024 | Parallel Backpropagation for Shared-Feature Visualization. Alexander Lappe, Anna Bognár, Ghazaleh Ghamkhari Nejad, Albert Mukovskiy, Lucas Martini, Martin A. Giese, Rufin Vogels |
| 2024 | ParallelEdits: Efficient Multi-Aspect Text-Driven Image Editing with Attention Grouping. Mingzhen Huang, Jialing Cai, Shan Jia, Vishnu Suresh Lokhande, Siwei Lyu |
| 2024 | Parallelizing Linear Transformers with the Delta Rule over Sequence Length. Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, Yoon Kim |
| 2024 | Parallelizing Model-based Reinforcement Learning Over the Sequence Length. Zirui Wang, Yue Deng, Junfeng Long, Yin Zhang |
| 2024 | Parameter Competition Balancing for Model Merging. Guodong Du, Junlin Lee, Jing Li, Runhua Jiang, Yifei Guo, Shuyang Yu, Hanting Liu, Sim Kuan Goh, Ho-Kin Tang, Daojing He, Min Zhang |
| 2024 | Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning. Wenke Huang, Mang Ye, Zekun Shi, Guancheng Wan, He Li, Bo Du |
| 2024 | Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts. Hang Guo, Tao Dai, Yuanchao Bai, Bin Chen, Xudong Ren, Zexuan Zhu, Shu-Tao Xia |
| 2024 | Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent. Liu Ziyin, Mingze Wang, Hongchao Li, Lei Wu |
| 2024 | Parameter-Inverted Image Pyramid Networks. Xizhou Zhu, Xue Yang, Zhaokai Wang, Hao Li, Wenhan Dou, Junqi Ge, Lewei Lu, Yu Qiao, Jifeng Dai |
| 2024 | Parameter-free Clipped Gradient Descent Meets Polyak. Yuki Takezawa, Han Bao, Ryoma Sato, Kenta Niwa, Makoto Yamada |
| 2024 | Parameterized Approximation Schemes for Fair-Range Clustering. Zhen Zhang, Xiaohong Chen, Limei Liu, Jie Chen, Junyu Huang, Qilong Feng |
| 2024 | Parametric model reduction of mean-field and stochastic systems via higher-order action matching. Jules Berman, Tobias Blickhan, Benjamin Peherstorfer |
| 2024 | Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation. Lingxiao Zhao, Xueying Ding, Leman Akoglu |
| 2024 | Parseval Regularization for Continual Reinforcement Learning. Wesley Chung, Lynn Cherif, Doina Precup, David Meger |
| 2024 | Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting. Jinliang Deng, Feiyang Ye, Du Yin, Xuan Song, Ivor W. Tsang, Hui Xiong |
| 2024 | Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits. Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu |
| 2024 | Partial Transportability for Domain Generalization. Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim |
| 2024 | Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics. William Qian, Jacob A. Zavatone-Veth, Benjamin S. Ruben, Cengiz Pehlevan |
| 2024 | Particle Semi-Implicit Variational Inference. Jen Ning Lim, Adam M. Johansen |
| 2024 | Paths to Equilibrium in Games. Bora Yongacoglu, Gürdal Arslan, Lacra Pavel, Serdar Yüksel |
| 2024 | PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator. Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew, Qiang Liu, Jiashi Feng |
| 2024 | Pearls from Pebbles: Improved Confidence Functions for Auto-labeling. Harit Vishwakarma, Yi Chen, Sui Jiet Tay, Satya Sai Srinath Namburi, Frederic Sala, Ramya Korlakai Vinayak |
| 2024 | Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking. Pranav Singh Chib, Pravendra Singh |
| 2024 | Pedestrian-Centric 3D Pre-collision Pose and Shape Estimation from Dashcam Perspective. Meijun Wang, Yu Meng, Zhongwei Qiu, Chao Zheng, Yan Xu, Pengxiaorui, Jian Gao |
| 2024 | PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications. Dingkang Yang, Jinjie Wei, Dongling Xiao, Shunli Wang, Tong Wu, Gang Li, Mingcheng Li, Shuaibing Wang, Jiawei Chen, Yue Jiang, Qingyao Xu, Ke Li, Peng Zhai, Lihua Zhang |
| 2024 | Penalty-based Methods for Simple Bilevel Optimization under Hölderian Error Bounds. Pengyu Chen, Xu Shi, Rujun Jiang, Jiulin Wang |
| 2024 | Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers. Markus Hiller, Krista A. Ehinger, Tom Drummond |
| 2024 | Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering. Zhihua Wen, Zhiliang Tian, Zexin Jian, Zhen Huang, Pei Ke, Yifu Gao, Minlie Huang, Dongsheng Li |
| 2024 | Perceptual Fairness in Image Restoration. Guy Ohayon, Michael Elad, Tomer Michaeli |
| 2024 | Performative Control for Linear Dynamical Systems. Songfu Cai, Fei Han, Xuanyu Cao |
| 2024 | Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis. Qiang Wu, Gechang Yao, Zhixi Feng, Shuyuan Yang |
| 2024 | Periodic agent-state based Q-learning for POMDPs. Amit Sinha, Matthieu Geist, Aditya Mahajan |
| 2024 | Perplexity-aware Correction for Robust Alignment with Noisy Preferences. Keyi Kong, Xilie Xu, Di Wang, Jingfeng Zhang, Mohan S. Kankanhalli |
| 2024 | Persistence Homology Distillation for Semi-supervised Continual Learning. Yan Fan, Yu Wang, Pengfei Zhu, Dongyue Chen, Qinghua Hu |
| 2024 | Persistent Homology for High-dimensional Data Based on Spectral Methods. Sebastian Damrich, Philipp Berens, Dmitry Kobak |
| 2024 | Persistent Test-time Adaptation in Recurring Testing Scenarios. Trung-Hieu Hoang, MinhDuc Vo, Minh Do |
| 2024 | PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models. Lemei Zhang, Peng Liu, Marcus Tiedemann Oekland Henriksboe, Even W. Lauvrak, Jon Atle Gulla, Heri Ramampiaro |
| 2024 | Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models. Shengchao Chen, Guodong Long, Jing Jiang, Chengqi Zhang |
| 2024 | Personalized Federated Learning via Feature Distribution Adaptation. Connor Mclaughlin, Lili Su |
| 2024 | Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning. Pouya M. Ghari, Yanning Shen |
| 2024 | Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments. Luca Barsellotti, Roberto Bigazzi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara |
| 2024 | Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization. Yuanpu Cao, Tianrong Zhang, Bochuan Cao, Ziyi Yin, Lu Lin, Fenglong Ma, Jinghui Chen |
| 2024 | Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning. Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, Natasha Jaques |
| 2024 | PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations. Jiatong Li, Renjun Hu, Kunzhe Huang, Yan Zhuang, Qi Liu, Mengxiao Zhu, Xing Shi, Wei Lin |
| 2024 | Pessimistic Backward Policy for GFlowNets. Hyosoon Jang, Yunhui Jang, Minsu Kim, Jinkyoo Park, Sungsoo Ahn |
| 2024 | Phased Consistency Models. Fu-Yun Wang, Zhaoyang Huang, Alexander William Bergman, Dazhong Shen, Peng Gao, Michael Lingelbach, Keqiang Sun, Weikang Bian, Guanglu Song, Yu Liu, Xiaogang Wang, Hongsheng Li |
| 2024 | PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging. Xin Cai, Zhiyuan You, Hailong Zhang, Jinwei Gu, Wentao Liu, Tianfan Xue |
| 2024 | PhyRecon: Physically Plausible Neural Scene Reconstruction. Junfeng Ni, Yixin Chen, Bohan Jing, Nan Jiang, Bin Wang, Bo Dai, Puhao Li, Yixin Zhu, Song-Chun Zhu, Siyuan Huang |
| 2024 | PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation. Chenrui Duan, Zelin Zang, Siyuan Li, Yongjie Xu, Stan Z. Li |
| 2024 | Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning. Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, Tie-Yan Liu |
| 2024 | Physically Compatible 3D Object Modeling from a Single Image. Minghao Guo, Bohan Wang, Pingchuan Ma, Tianyuan Zhang, Crystal Elaine Owens, Chuang Gan, Josh Tenenbaum, Kaiming He, Wojciech Matusik |
| 2024 | Physics-Constrained Comprehensive Optical Neural Networks. Yanbing Liu, Jianwei Qin, Yan Liu, Xi Yue, Xun Liu, Guoqing Wang, Tianyu Li, Fangwei Ye, Wei Li |
| 2024 | Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling. Zijie Huang, Wanjia Zhao, Jingdong Gao, Ziniu Hu, Xiao Luo, Yadi Cao, Yuanzhou Chen, Yizhou Sun, Wei Wang |
| 2024 | Physics-Informed Variational State-Space Gaussian Processes. Oliver Hamelijnck, Arno Solin, Theodoros Damoulas |
| 2024 | Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization. Michal Balcerak, Tamaz Amiranashvili, Andreas Wagner, Jonas Weidner, Petr Karnakov, Johannes C. Paetzold, Ivan Ezhov, Petros Koumoutsakos, Benedikt Wiestler, Bjoern H. Menze |
| 2024 | Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees. Taiki Miyagawa, Takeru Yokota |
| 2024 | PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models. Fanxu Meng, Zhaohui Wang, Muhan Zhang |
| 2024 | Piecewise deterministic generative models. Andrea Bertazzi, Dario Shariatian, Umut Simsekli, Eric Moulines, Alain Durmus |
| 2024 | Piecewise-Stationary Bandits with Knapsacks. Xilin Zhang, Wang Chi Cheung |
| 2024 | Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction. Qiang Liu, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang |
| 2024 | Pipeline Parallelism with Controllable Memory. Penghui Qi, Xinyi Wan, Nyamdavaa Amar, Min Lin |
| 2024 | Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs. Liyi Chen, Panrong Tong, Zhongming Jin, Ying Sun, Jieping Ye, Hui Xiong |
| 2024 | Plant-and-Steal: Truthful Fair Allocations via Predictions. Ilan Reuven Cohen, Alon Eden, Talya Eden, Arsen Vasilyan |
| 2024 | Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning. Haoyi Zhu, Yating Wang, Di Huang, Weicai Ye, Wanli Ouyang, Tong He |
| 2024 | Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis. Hongyu Sun, Qiuhong Ke, Yongcai Wang, Wang Chen, Kang Yang, Deying Li, Jianfei Cai |
| 2024 | PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection. Qihang Zhou, Jiangtao Yan, Shibo He, Wenchao Meng, Jiming Chen |
| 2024 | PointMamba: A Simple State Space Model for Point Cloud Analysis. Dingkang Liang, Xin Zhou, Wei Xu, Xingkui Zhu, Zhikang Zou, Xiaoqing Ye, Xiao Tan, Xiang Bai |
| 2024 | Poisson Variational Autoencoder. Hadi Vafaii, Dekel Galor, Jacob L. Yates |
| 2024 | Policy Aggregation. Parand A. Alamdari, Soroush Ebadian, Ariel D. Procaccia |
| 2024 | Policy Improvement using Language Feedback Models. Victor Zhong, Dipendra Misra, Xingdi Yuan, Marc-Alexandre Côté |
| 2024 | Policy Learning from Tutorial Books via Understanding, Rehearsing and Introspecting. Xiong-Hui Chen, Ziyan Wang, Yali Du, Shengyi Jiang, Meng Fang, Yang Yu, Jun Wang |
| 2024 | Policy Mirror Descent with Lookahead. Kimon Protopapas, Anas Barakat |
| 2024 | Policy Optimization for Robust Average Reward MDPs. Zhongchang Sun, Sihong He, Fei Miao, Shaofeng Zou |
| 2024 | Policy-shaped prediction: avoiding distractions in model-based reinforcement learning. Miles Hutson, Isaac Kauvar, Nick Haber |
| 2024 | Polyhedral Complex Derivation from Piecewise Trilinear Networks. Jin-Hwa Kim |
| 2024 | Polynomial-Time Computation of Exact $\Phi$-Equilibria in Polyhedral Games. Gabriele Farina, Charilaos Pipis |
| 2024 | Poseidon: Efficient Foundation Models for PDEs. Maximilian Herde, Bogdan Raonic, Tobias Rohner, Roger Käppeli, Roberto Molinaro, Emmanuel de Bézenac, Siddhartha Mishra |
| 2024 | Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure. Hanseul Cho, Jaeyoung Cha, Pranjal Awasthi, Srinadh Bhojanapalli, Anupam Gupta, Chulhee Yun |
| 2024 | Post-Hoc Reversal: Are We Selecting Models Prematurely? Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary C. Lipton |
| 2024 | Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation. Kyung Jin Seo, Junghoon Seo, Hanseok Jeong, Sangpil Kim, Sang Ho Yoon |
| 2024 | PowerGraph: A power grid benchmark dataset for graph neural networks. Anna Varbella, Kenza Amara, Blazhe Gjorgiev, Mennatallah El-Assady, Giovanni Sansavini |
| 2024 | PowerPM: Foundation Model for Power Systems. Shihao Tu, Yupeng Zhang, Jing Zhang, Zhendong Fu, Yin Zhang, Yang Yang |
| 2024 | Practical 0.385-Approximation for Submodular Maximization Subject to a Cardinality Constraint. Murad Tukan, Loay Mualem, Moran Feldman |
| 2024 | Practical Bayesian Algorithm Execution via Posterior Sampling. Chu Xin Cheng, Raul Astudillo, Thomas A. Desautels, Yisong Yue |
| 2024 | Practical Shuffle Coding. Julius Kunze, Daniel Severo, Jan-Willem van de Meent, James Townsend |
| 2024 | Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation. Haoqi Yuan, Yuhui Fu, Feiyang Xie, Zongqing Lu |
| 2024 | Pre-trained Large Language Models Use Fourier Features to Compute Addition. Tianyi Zhou, Deqing Fu, Vatsal Sharan, Robin Jia |
| 2024 | Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control. Gunshi Gupta, Karmesh Yadav, Yarin Gal, Dhruv Batra, Zsolt Kira, Cong Lu, Tim G. J. Rudner |
| 2024 | Pre-training Differentially Private Models with Limited Public Data. Zhiqi Bu, Xinwei Zhang, Sheng Zha, Mingyi Hong, George Karypis |
| 2024 | Precipitation Downscaling with Spatiotemporal Video Diffusion. Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher S. Bretherton, Stephan Mandt |
| 2024 | Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks. Chiraag Kaushik, Justin Romberg, Vidya Muthukumar |
| 2024 | Predicting Future Actions of Reinforcement Learning Agents. Stephen Chung, Scott Niekum, David Krueger |
| 2024 | Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms. Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya, Devdatt P. Dubhashi, Alexandru Gheorghiu |
| 2024 | Predicting Label Distribution from Ternary Labels. Yunan Lu, Xiuyi Jia |
| 2024 | Predicting the Performance of Foundation Models via Agreement-on-the-Line. Rahul Saxena, Taeyoun Kim, Aman Mehra, Christina Baek, J. Zico Kolter, Aditi Raghunathan |
| 2024 | Prediction with Action: Visual Policy Learning via Joint Denoising Process. Yanjiang Guo, Yucheng Hu, Jianke Zhang, Yen-Jen Wang, Xiaoyu Chen, Chaochao Lu, Jianyu Chen |
| 2024 | Prediction-Powered Ranking of Large Language Models. Ivi Chatzi, Eleni Straitouri, Suhas Thejaswi, Manuel Gomez Rodriguez |
| 2024 | Predictive Attractor Models. Ramy Mounir, Sudeep Sarkar |
| 2024 | Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning. Bei Li, Tong Zheng, Rui Wang, Jiahao Liu, Qingyan Guo, Junliang Guo, Xu Tan, Tong Xiao, Jingbo Zhu, Jingang Wang, Xunliang Cai |
| 2024 | PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference. Kendong Liu, Zhiyu Zhu, Chuanhao Li, Hui Liu, Huanqiang Zeng, Junhui Hou |
| 2024 | Preference Alignment with Flow Matching. Minu Kim, Yongsik Lee, Sehyeok Kang, Jihwan Oh, Song Chong, Se-Young Yun |
| 2024 | Preference Learning Algorithms Do Not Learn Preference Rankings. Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Qiuyi (Richard) Zhang, Rajesh Ranganath, Kyunghyun Cho |
| 2024 | Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice. Sebastiaan De Peuter, Shibei Zhu, Yujia Guo, Andrew Howes, Samuel Kaski |
| 2024 | Preference-based Pure Exploration. Apurv Shukla, Debabrota Basu |
| 2024 | Preferential Normalizing Flows. Petrus Mikkola, Luigi Acerbi, Arto Klami |
| 2024 | Pretrained Optimization Model for Zero-Shot Black Box Optimization. Xiaobin Li, Kai Wu, Yujian Betterest Li, Xiaoyu Zhang, Handing Wang, Jing Liu |
| 2024 | Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context. Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu |
| 2024 | Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs. Md. Ashiqur Rahman, Robert Joseph George, Mogab Elleithy, Daniel V. Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Animashree Anandkumar |
| 2024 | Pretraining with Random Noise for Fast and Robust Learning without Weight Transport. Jeonghwan Cheon, Sang Wan Lee, Se-Bum Paik |
| 2024 | Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization. Junlin He, Jinxiao Du, Wei Ma |
| 2024 | Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization. Junlin He, Jinxiao Du, Susu Xu, Wei Ma |
| 2024 | Pricing and Competition for Generative AI. Rafid Mahmood |
| 2024 | Principled Bayesian Optimization in Collaboration with Human Experts. Wenjie Xu, Masaki Adachi, Colin N. Jones, Michael A. Osborne |
| 2024 | Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors. Zihui Wu, Yu Sun, Yifan Chen, Bingliang Zhang, Yisong Yue, Katherine L. Bouman |
| 2024 | Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy. Zeki Kazan, Jerome P. Reiter |
| 2024 | Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs. Yuxuan Qiao, Haodong Duan, Xinyu Fang, Junming Yang, Lin Chen, Songyang Zhang, Jiaqi Wang, Dahua Lin, Kai Chen |
| 2024 | PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques. Derui Zhu, Dingfan Chen, Xiongfei Wu, Jiahui Geng, Zhuo Li, Jens Grossklags, Lei Ma |
| 2024 | PrivCirNet: Efficient Private Inference via Block Circulant Transformation. Tianshi Xu, Lemeng Wu, Runsheng Wang, Meng Li |
| 2024 | Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models. Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, Nicholas Carlini |
| 2024 | Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training. Kristjan H. Greenewald, Yuancheng Yu, Hao Wang, Kai Xu |
| 2024 | PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action. Yijia Shao, Tianshi Li, Weiyan Shi, Yanchen Liu, Diyi Yang |
| 2024 | Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry. Raef Bassily, Cristóbal Guzmán, Michael Menart |
| 2024 | Private Attribute Inference from Images with Vision-Language Models. Batuhan Tömekçe, Mark Vero, Robin Staab, Martin T. Vechev |
| 2024 | Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust. Hongjie Chen, Jingqiu Ding, Yiding Hua, David Steurer |
| 2024 | Private Geometric Median. Mahdi Haghifam, Thomas Steinke, Jonathan R. Ullman |
| 2024 | Private Online Learning via Lazy Algorithms. Hilal Asi, Tomer Koren, Daogao Liu, Kunal Talwar |
| 2024 | Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions. Hilal Asi, Daogao Liu, Kevin Tian |
| 2024 | Private and Personalized Frequency Estimation in a Federated Setting. Amrith Setlur, Vitaly Feldman, Kunal Talwar |
| 2024 | ProEdit: Simple Progression is All You Need for High-Quality 3D Scene Editing. Jun-Kun Chen, Yu-Xiong Wang |
| 2024 | ProG: A Graph Prompt Learning Benchmark. Chenyi Zi, Haihong Zhao, Xiangguo Sun, Yiqing Lin, Hong Cheng, Jia Li |
| 2024 | ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention. Mingchen Li, Yang Tan, Xinzhu Ma, Bozitao Zhong, Huiqun Yu, Ziyi Zhou, Wanli Ouyang, Bingxin Zhou, Pan Tan, Liang Hong |
| 2024 | ProTransformer: Robustify Transformers via Plug-and-Play Paradigm. Zhichao Hou, Weizhi Gao, Yuchen Shen, Feiyi Wang, Xiaorui Liu |
| 2024 | ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons. Jiawen Zhang, Xumeng Wen, Zhenwei Zhang, Shun Zheng, Jia Li, Jiang Bian |
| 2024 | Probabilistic Conformal Distillation for Enhancing Missing Modality Robustness. Mengxi Chen, Fei Zhang, Zihua Zhao, Jiangchao Yao, Ya Zhang, Yanfeng Wang |
| 2024 | Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics. Yenho Chen, Noga Mudrik, Kyle A. Johnsen, Sankaraleengam Alagapan, Adam S. Charles, Christopher Rozell |
| 2024 | Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. Pei-Yau Weng, Minh Hoang, Lam M. Nguyen, My T. Thai, Lily Weng, Trong Nghia Hoang |
| 2024 | Probabilistic Graph Rewiring via Virtual Nodes. Chendi Qian, Andrei Manolache, Christopher Morris, Mathias Niepert |
| 2024 | Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten |
| 2024 | Probabilistic size-and-shape functional mixed models. Fangyi Wang, Karthik Bharath, Oksana A. Chkrebtii, Sebastian Kurtek |
| 2024 | Probablistic Emulation of a Global Climate Model with Spherical DYffusion. Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose Yu |
| 2024 | Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach. Lei Ding, Yang Hu, Nicole Denier, Enze Shi, Junxi Zhang, Qirui Hu, Karen D. Hughes, Linglong Kong, Bei Jiang |
| 2024 | Probing the Decision Boundaries of In-context Learning in Large Language Models. Siyan Zhao, Tung Nguyen, Aditya Grover |
| 2024 | Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation. Kun Yuan, Vinkle Srivastav, Nassir Navab, Nicolas Padoy |
| 2024 | ProgressGym: Alignment with a Millennium of Moral Progress. Tianyi Qiu, Yang Zhang, Xuchuan Huang, Jasmine Xinze Li, Jiaming Ji, Yaodong Yang |
| 2024 | Progressive Entropic Optimal Transport Solvers. Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi |
| 2024 | Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images. Zihan Lu, Chenxu Wang, Chunyan Xu, Xiangwei Zheng, Zhen Cui |
| 2024 | Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions. Will Ma, Pan Xu |
| 2024 | Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars. Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low |
| 2024 | Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation. Abhinav Jain, Swarat Chaudhuri, Thomas W. Reps, Christopher M. Jermaine |
| 2024 | Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models. Cong Wan, Yuhang He, Xiang Song, Yihong Gong |
| 2024 | PromptFix: You Prompt and We Fix the Photo. Yongsheng Yu, Ziyun Zeng, Hang Hua, Jianlong Fu, Jiebo Luo |
| 2024 | Propensity Score Alignment of Unpaired Multimodal Data. Johnny Xi, Jana Osea, Zuheng Xu, Jason S. Hartford |
| 2024 | Proportional Fairness in Clustering: A Social Choice Perspective. Leon Kellerhals, Jannik Peters |
| 2024 | Proportional Fairness in Non-Centroid Clustering. Ioannis Caragiannis, Evi Micha, Nisarg Shah |
| 2024 | Prospective Learning: Learning for a Dynamic Future. Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T. Vogelstein, Pratik Chaudhari |
| 2024 | Prospective Representation Learning for Non-Exemplar Class-Incremental Learning. Wuxuan Shi, Mang Ye |
| 2024 | ProtGO: Function-Guided Protein Modeling for Unified Representation Learning. Bozhen Hu, Cheng Tan, Yongjie Xu, Zhangyang Gao, Jun Xia, Lirong Wu, Stan Z. Li |
| 2024 | Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach. Yarin Bar, Shalev Shaer, Yaniv Romano |
| 2024 | Protecting Your LLMs with Information Bottleneck. Zichuan Liu, Zefan Wang, Linjie Xu, Jinyu Wang, Lei Song, Tianchun Wang, Chunlin Chen, Wei Cheng, Jiang Bian |
| 2024 | Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer. Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin |
| 2024 | Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery. Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe, Zhun Zhong |
| 2024 | ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Field. Kiyohiro Nakayama, Mikaela Angelina Uy, Yang You, Ke Li, Leonidas J. Guibas |
| 2024 | Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks. Zhenghao Xu, Yuqing Wang, Tuo Zhao, Rachel Ward, Molei Tao |
| 2024 | Provable Benefit of Cutout and CutMix for Feature Learning. Junsoo Oh, Chulhee Yun |
| 2024 | Provable Benefits of Complex Parameterizations for Structured State Space Models. Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen |
| 2024 | Provable Editing of Deep Neural Networks using Parametric Linear Relaxation. Zhe Tao, Aditya V. Thakur |
| 2024 | Provable Partially Observable Reinforcement Learning with Privileged Information. Yang Cai, Xiangyu Liu, Argyris Oikonomou, Kaiqing Zhang |
| 2024 | Provable Posterior Sampling with Denoising Oracles via Tilted Transport. Joan Bruna, Jiequn Han |
| 2024 | Provable Tempered Overfitting of Minimal Nets and Typical Nets. Itamar Harel, William Hoza, Gal Vardi, Itay Evron, Nati Srebro, Daniel Soudry |
| 2024 | Provable and Efficient Dataset Distillation for Kernel Ridge Regression. Yilan Chen, Wei Huang, Lily Weng |
| 2024 | Provably Efficient Interactive-Grounded Learning with Personalized Reward. Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro |
| 2024 | Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation. Long-Fei Li, Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou |
| 2024 | Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling. Junyi Li, Heng Huang |
| 2024 | Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer. Zhihan Liu, Miao Lu, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose H. Blanchet, Zhaoran Wang |
| 2024 | Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes. Jerry Yao-Chieh Hu, Dennis Wu, Han Liu |
| 2024 | Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction. Xingyu Xu, Yuejie Chi |
| 2024 | Provably Safe Neural Network Controllers via Differential Dynamic Logic. Samuel Teuber, Stefan Mitsch, André Platzer |
| 2024 | Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning. Dake Bu, Wei Huang, Andi Han, Atsushi Nitanda, Taiji Suzuki, Qingfu Zhang, Hau-San Wong |
| 2024 | Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation. Tian Xu, Zhilong Zhang, Ruishuo Chen, Yihao Sun, Yang Yu |
| 2024 | Proving Olympiad Algebraic Inequalities without Human Demonstrations. Chenrui Wei, Mengzhou Sun, Wei Wang |
| 2024 | Proving Theorems Recursively. Haiming Wang, Huajian Xin, Zhengying Liu, Wenda Li, Yinya Huang, Jianqiao Lu, Zhicheng Yang, Jing Tang, Jian Yin, Zhenguo Li, Xiaodan Liang |
| 2024 | Proximal Causal Inference With Text Data. Jacob M. Chen, Rohit Bhattacharya, Katherine A. Keith |
| 2024 | ProxyFusion: Face Feature Aggregation Through Sparse Experts. Bhavin Jawade, Alexander Stone, Deen Dayal Mohan, Xiao Wang, Srirangaraj Setlur, Venu Govindaraju |
| 2024 | Prune and Repaint: Content-Aware Image Retargeting for any Ratio. Feihong Shen, Chao Li, Yifeng Geng, Yongjian Deng, Hao Chen |
| 2024 | Pruning neural network models for gene regulatory dynamics using data and domain knowledge. Intekhab Hossain, Jonas Fischer, Rebekka Burkholz, John Quackenbush |
| 2024 | Pseudo-Private Data Guided Model Inversion Attacks. Xiong Peng, Bo Han, Feng Liu, Tongliang Liu, Mingyuan Zhou |
| 2024 | Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising. Yuhui Quan, Tianxiang Zheng, Hui Ji |
| 2024 | PuLID: Pure and Lightning ID Customization via Contrastive Alignment. Zinan Guo, Yanze Wu, Zhuowei Chen, Lang Chen, Peng Zhang, Qian He |
| 2024 | Public-data Assisted Private Stochastic Optimization: Power and Limitations. Enayat Ullah, Michael Menart, Raef Bassily, Cristóbal Guzmán, Raman Arora |
| 2024 | Pure Message Passing Can Estimate Common Neighbor for Link Prediction. Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla |
| 2024 | PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics. Omead Pooladzandi, Sunay Bhat, Jeffrey Jiang, Alexander Branch, Gregory J. Pottie |
| 2024 | PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition. George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, Swarat Chaudhuri |
| 2024 | Putting Gale & Shapley to Work: Guaranteeing Stability Through Learning. Hadi Hosseini, Sanjukta Roy, Duohan Zhang |
| 2024 | Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model. Jing Zhang, Linjiajie Fang, Kexin Shi, Wenjia Wang, Bingyi Jing |
| 2024 | Q-VLM: Post-training Quantization for Large Vision-Language Models. Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, Jiwen Lu |
| 2024 | QBB: Quantization with Binary Bases for LLMs. Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos |
| 2024 | QGFN: Controllable Greediness with Action Values. Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio |
| 2024 | QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers. Haozhe Chen, Ang Li, Ethan Che, Jing Dong, Tianyi Peng, Hongseok Namkoong |
| 2024 | QKFormer: Hierarchical Spiking Transformer using Q-K Attention. Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu, Liwei Huang, Xiaopeng Fan, Li Yuan, Zhengyu Ma, Huihui Zhou, Yonghong Tian |
| 2024 | QT-ViT: Improving Linear Attention in ViT with Quadratic Taylor Expansion. Yixing Xu, Chao Li, Dong Li, Xiao Sheng, Fan Jiang, Lu Tian, Emad Barsoum |
| 2024 | QTIP: Quantization with Trellises and Incoherence Processing. Albert Tseng, Qingyao Sun, David Hou, Christopher De Sa |
| 2024 | QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos. Sharath Girish, Tianye Li, Amrita Mazumdar, Abhinav Shrivastava, David Luebke, Shalini De Mello |
| 2024 | QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization. Qi Song, Tianxiang Gong, Shiqi Gao, Haoyi Zhou, Jianxin Li |
| 2024 | QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation. Gonçalo Rui Alves Faria, Sweta Agrawal, António Farinhas, Ricardo Rei, José Guilherme Camargo de Souza, André F. T. Martins |
| 2024 | QVAE-Mole: The Quantum VAE with Spherical Latent Variable Learning for 3-D Molecule Generation. Huaijin Wu, Xinyu Ye, Junchi Yan |
| 2024 | QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs. Mohammad Shahverdikondori, Ehsan Mokhtarian, Negar Kiyavash |
| 2024 | QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, James Hensman |
| 2024 | QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model. Fei Xie, Weijia Zhang, Zhongdao Wang, Chao Ma |
| 2024 | Quadratic Quantum Variational Monte Carlo. Baiyu Su, Qiang Liu |
| 2024 | Qualitative Mechanism Independence. Oliver Richardson, Spencer J. Peters, Joseph Y. Halpern |
| 2024 | Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing. Chu Zhou, Yixing Liu, Chao Xu, Boxin Shi |
| 2024 | QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation. Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan, Di Luo, Marin Soljacic |
| 2024 | Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner. Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar |
| 2024 | Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing. Letian Peng, Jingbo Shang |
| 2024 | Quantifying the Gain in Weak-to-Strong Generalization. Moses Charikar, Chirag Pabbaraju, Kirankumar Shiragur |
| 2024 | Quantitative Convergences of Lie Group Momentum Optimizers. Lingkai Kong, Molei Tao |
| 2024 | Quantum Algorithms for Non-smooth Non-convex Optimization. Chengchang Liu, Chaowen Guan, Jianhao He, John C. S. Lui |
| 2024 | Quantum Deep Equilibrium Models. Philipp Schleich, Marta Skreta, Lasse Bjørn Kristensen, Rodrigo A. Vargas-Hernández, Alán Aspuru-Guzik |
| 2024 | Quantum algorithm for large-scale market equilibrium computation. Po-Wei Huang, Patrick Rebentrost |
| 2024 | Quasi-Bayes meets Vines. David Huk, Yuanhe Zhang, Ritabrata Dutta, Mark Steel |
| 2024 | QueST: Self-Supervised Skill Abstractions for Learning Continuous Control. Atharva Mete, Haotian Xue, Albert Wilcox, Yongxin Chen, Animesh Garg |
| 2024 | Query-Based Adversarial Prompt Generation. Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, Milad Nasr |
| 2024 | Query-Efficient Correlation Clustering with Noisy Oracle. Yuko Kuroki, Atsushi Miyauchi, Francesco Bonchi, Wei Chen |
| 2024 | Questioning the Survey Responses of Large Language Models. Ricardo Dominguez-Olmedo, Moritz Hardt, Celestine Mendler-Dünner |
| 2024 | Queueing Matching Bandits with Preference Feedback. Jung-Hun Kim, Min-hwan Oh |
| 2024 | R Ruyi Zha, Tao Jun Lin, Yuanhao Cai, Jiwen Cao, Yanhao Zhang, Hongdong Li |
| 2024 | RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning. Yujie Zhao, Jose E. Aguilar Escamilla, Weyl Lu, Huazheng Wang |
| 2024 | RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation. Dongyu Ru, Lin Qiu, Xiangkun Hu, Tianhang Zhang, Peng Shi, Shuaichen Chang, Cheng Jiayang, Cunxiang Wang, Shichao Sun, Huanyu Li, Zizhao Zhang, Binjie Wang, Jiarong Jiang, Tong He, Zhiguo Wang, Pengfei Liu, Yue Zhang, Zheng Zhang |
| 2024 | RAGraph: A General Retrieval-Augmented Graph Learning Framework. Xinke Jiang, Rihong Qiu, Yongxin Xu, Wentao Zhang, Yichen Zhu, Ruizhe Zhang, Yuchen Fang, Chu Xu, Junfeng Zhao, Yasha Wang |
| 2024 | RAMP: Boosting Adversarial Robustness Against Multiple l Enyi Jiang, Gagandeep Singh |
| 2024 | RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees. Xun Xian, Ganghua Wang, Xuan Bi, Jayanth Srinivasa, Ashish Kundu, Mingyi Hong, Jie Ding |
| 2024 | RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-based 3D Neural Modeling. Tianhang Wang, Fan Lu, Zehan Zheng, Zhijun Li, Guang Chen, Changjun Jiang |
| 2024 | RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation. Anton Antonov, Andrey Moskalenko, Denis Shepelev, Alexander Krapukhin, Konstantin Soshin, Anton Konushin, Vlad Shakhuro |
| 2024 | REBEL: Reinforcement Learning via Regressing Relative Rewards. Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, Drew Bagnell, Jason D. Lee, Wen Sun |
| 2024 | REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR. Liang-Hsuan Tseng, En-Pei Hu, Cheng-Han Chiang, Yuan Tseng, Hung-yi Lee, Lin-Shan Lee, Shao-Hua Sun |
| 2024 | REDUCR: Robust Data Downsampling using Class Priority Reweighting. William Bankes, George Hughes, Ilija Bogunovic, Zi Wang |
| 2024 | RETR: Multi-View Radar Detection Transformer for Indoor Perception. Ryoma Yataka, Adriano Cardace, Perry Wang, Petros Boufounos, Ryuhei Takahashi |
| 2024 | RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation. Peihua Mai, Ran Yan, Yan Pang |
| 2024 | RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D Referring Expression Segmentation. Changli Wu, Qi Chen, Jiayi Ji, Haowei Wang, Yiwei Ma, You Huang, Gen Luo, Hao Fei, Xiaoshuai Sun, Rongrong Ji |
| 2024 | RGFN: Synthesizable Molecular Generation Using GFlowNets. Michal Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer van der Sloot, Piotr Gainski, Yoshua Bengio, Cheng-Hao Liu, Mike Tyers, Robert A. Batey |
| 2024 | RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space. Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong, Gina C. Adam, Nathaniel D. Bastian, Tian Lan |
| 2024 | RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation. Jeongyeol Kwon, Shie Mannor, Constantine Caramanis, Yonathan Efroni |
| 2024 | RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar |
| 2024 | RL-GPT: Integrating Reinforcement Learning and Code-as-policy. Shaoteng Liu, Haoqi Yuan, Minda Hu, Yanwei Li, Yukang Chen, Shu Liu, Zongqing Lu, Jiaya Jia |
| 2024 | RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-Identification. Lei Tan, Yukang Zhang, Keke Han, Pingyang Dai, Yan Zhang, Yongjian Wu, Rongrong Ji |
| 2024 | RMLR: Extending Multinomial Logistic Regression into General Geometries. Ziheng Chen, Yue Song, Rui Wang, Xiaojun Wu, Nicu Sebe |
| 2024 | ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization. Huayang Huang, Yu Wu, Qian Wang |
| 2024 | ROIDICE: Offline Return on Investment Maximization for Efficient Decision Making. Woosung Kim, Hayeong Lee, Jongmin Lee, Byung-Jun Lee |
| 2024 | RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions. Ziyao Zeng, Yangchao Wu, Hyoungseob Park, Daniel Wang, Fengyu Yang, Stefano Soatto, Dong Lao, Byung-Woo Hong, Alex Wong |
| 2024 | RTify: Aligning Deep Neural Networks with Human Behavioral Decisions. Yu-Ang Cheng, Ivan F. Rodriguez Rodriguez, Sixuan Chen, Kohitij Kar, Takeo Watanabe, Thomas Serre |
| 2024 | RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models. Zhuoran Jin, Pengfei Cao, Chenhao Wang, Zhitao He, Hongbang Yuan, Jiachun Li, Yubo Chen, Kang Liu, Jun Zhao |
| 2024 | RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models. Maya Varma, Jean-Benoit Delbrouck, Zhihong Chen, Akshay Chaudhari, Curtis P. Langlotz |
| 2024 | Rad-NeRF: Ray-decoupled Training of Neural Radiance Field. Lidong Guo, Xuefei Ning, Yonggan Fu, Tianchen Zhao, Zhuoliang Kang, Jincheng Yu, Yingyan (Celine) Lin, Yu Wang |
| 2024 | RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar. Fangqiang Ding, Xiangyu Wen, Yunzhou Zhu, Yiming Li, Chris Xiaoxuan Lu |
| 2024 | Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts. Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob N. Foerster, Tim Rocktäschel, Roberta Raileanu |
| 2024 | RanDumb: Random Representations Outperform Online Continually Learned Representations. Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru, Philip Torr, Ozan Sener, Puneet K. Dokania |
| 2024 | RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks. Guglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino |
| 2024 | Random Cycle Coding: Lossless Compression of Cluster Assignments via Bits-Back Coding. Daniel Severo, Ashish Khisti, Alireza Makhzani |
| 2024 | Random Function Descent. Felix Benning, Leif Döring |
| 2024 | Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation. Wooseong Cho, Taehyun Hwang, Joongkyu Lee, Min-hwan Oh |
| 2024 | Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu |
| 2024 | Randomized Sparse Matrix Compression for Large-Scale Constrained Optimization in Cancer Radiotherapy. Shima Adeli, Mojtaba Tefagh, Gourav Jhanwar, Masoud Zarepisheh |
| 2024 | Randomized Strategic Facility Location with Predictions. Eric Balkanski, Vasilis Gkatzelis, Golnoosh Shahkarami |
| 2024 | Randomized Truthful Auctions with Learning Agents. Gagan Aggarwal, Anupam Gupta, Andrés Perlroth, Grigoris Velegkas |
| 2024 | Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces. Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter, Volkan Cevher, John Lygeros |
| 2024 | RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs. Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, Bryan Catanzaro |
| 2024 | RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier. Pin-Yen Huang, Szu-Wei Fu, Yu Tsao |
| 2024 | Rapid Plug-in Defenders. Kai Wu, Yujian Betterest Li, Jian Lou, Xiaoyu Zhang, Handing Wang, Jing Liu |
| 2024 | RashomonGB: Analyzing the Rashomon Effect and Mitigating Predictive Multiplicity in Gradient Boosting. Hsiang Hsu, Ivan Brugere, Shubham Sharma, Freddy Lécué, Richard Chen |
| 2024 | Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving. Theodore Tsesmelis, Luca Palmieri, Marina Khoroshiltseva, Adeela Islam, Gur Elkin, Ofir Itzhak Shahar, Gianluca Scarpellini, Stefano Fiorini, Yaniv Ohayon, Nadav Alali, Sinem Aslan, Pietro Morerio, Sebastiano Vascon, Elena Gravina, Maria Cristina Napolitano, Giuseppe Scarpati, Gabriel Zuchtriegel, Alexandra Spühler, Michel E. Fuchs, Stuart James, Ohad Ben-Shahar, Marcello Pelillo, Alessio Del Bue |
| 2024 | ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution. Haoran Ye, Jiarui Wang, Zhiguang Cao, Federico Berto, Chuanbo Hua, Haeyeon Kim, Jinkyoo Park, Guojie Song |
| 2024 | ReF-LDM: A Latent Diffusion Model for Reference-based Face Image Restoration. Chi-Wei Hsiao, Yu-Lun Liu, Cheng-Kun Yang, Sheng-Po Kuo, Kevin Jou, Chia-Ping Chen |
| 2024 | ReFIR: Grounding Large Restoration Models with Retrieval Augmentation. Hang Guo, Tao Dai, Zhihao Ouyang, Taolin Zhang, Yaohua Zha, Bin Chen, Shu-Tao Xia |
| 2024 | ReFT: Representation Finetuning for Language Models. Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D. Manning, Christopher Potts |
| 2024 | ReGS: Reference-based Controllable Scene Stylization with Gaussian Splatting. Yiqun Mei, Jiacong Xu, Vishal M. Patel |
| 2024 | ReLIZO: Sample Reusable Linear Interpolation-based Zeroth-order Optimization. Xiaoxing Wang, Xiaohan Qin, Xiaokang Yang, Junchi Yan |
| 2024 | ReMAP: Neural Model Reprogramming with Network Inversion and Retrieval-Augmented Mapping for Adaptive Motion Forecasting. Sharmita Dey, Sarath Ravindran Nair |
| 2024 | ReMI: A Dataset for Reasoning with Multiple Images. Mehran Kazemi, Nishanth Dikkala, Ankit Anand, Petar Devic, Ishita Dasgupta, Fangyu Liu, Bahare Fatemi, Pranjal Awasthi, Sreenivas Gollapudi, Dee Guo, Ahmed Qureshi |
| 2024 | ReMoDetect: Reward Models Recognize Aligned LLM's Generations. Hyunseok Lee, Jihoon Tack, Jinwoo Shin |
| 2024 | ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization. Luca Eyring, Shyamgopal Karthik, Karsten Roth, Alexey Dosovitskiy, Zeynep Akata |
| 2024 | ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search. Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, Jie Tang |
| 2024 | ReVideo: Remake a Video with Motion and Content Control. Chong Mou, Mingdeng Cao, Xintao Wang, Zhaoyang Zhang, Ying Shan, Jian Zhang |
| 2024 | ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos. Jr-Jen Chen, Yu-Chien Liao, Hsi-Che Lin, Yu-Chu Yu, Yen-Chun Chen, Yu-Chiang Frank Wang |
| 2024 | ReactZyme: A Benchmark for Enzyme-Reaction Prediction. Chenqing Hua, Bozitao Zhong, Sitao Luan, Liang Hong, Guy Wolf, Doina Precup, Shuangjia Zheng |
| 2024 | Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design. Ruisi Cai, Yeonju Ro, Geon-Woo Kim, Peihao Wang, Babak Ehteshami Bejnordi, Aditya Akella, Zhangyang Wang |
| 2024 | Real-Time Recurrent Learning using Trace Units in Reinforcement Learning. Esraa Elelimy, Adam White, Michael Bowling, Martha White |
| 2024 | Real-Time Selection Under General Constraints via Predictive Inference. Yuyang Huo, Lin Lu, Haojie Ren, Changliang Zou |
| 2024 | Real-time Core-Periphery Guided ViT with Smart Data Layout Selection on Mobile Devices. Zhihao Shu, Xiaowei Yu, Zihao Wu, Wenqi Jia, Yinchen Shi, Miao Yin, Tianming Liu, Dajiang Zhu, Wei Niu |
| 2024 | Real-time Stereo-based 3D Object Detection for Streaming Perception. Changcai Li, Zonghua Gu, Gang Chen, Libo Huang, Wei Zhang, Huihui Zhou |
| 2024 | Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network. Chengyu Fang, Chunming He, Fengyang Xiao, Yulun Zhang, Longxiang Tang, Yuelin Zhang, Kai Li, Xiu Li |
| 2024 | RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models. Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu, Kai-Ni Wang, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, Bin Cui |
| 2024 | RealMAN: A Real-Recorded and Annotated Microphone Array Dataset for Dynamic Speech Enhancement and Localization. Bing Yang, Changsheng Quan, Yabo Wang, Pengyu Wang, Yujie Yang, Ying Fang, Nian Shao, Hui Bu, Xin Xu, Xiaofei Li |
| 2024 | Realizable H-Consistent and Bayes-Consistent Loss Functions for Learning to Defer. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2024 | Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving. Haochen Liu, Li Chen, Yu Qiao, Chen Lv, Hongyang Li |
| 2024 | Reasons and Solutions for the Decline in Model Performance after Editing. Xiusheng Huang, Jiaxiang Liu, Yequan Wang, Kang Liu |
| 2024 | Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training. Yanlai Yang, Matt Jones, Michael C. Mozer, Mengye Ren |
| 2024 | Reciprocal Learning. Julian Rodemann, Christoph Jansen, Georg Schollmeyer |
| 2024 | Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agents. John L. Zhou, Weizhe Hong, Jonathan C. Kao |
| 2024 | Recognize Any Regions. Haosen Yang, Chuofan Ma, Bin Wen, Yi Jiang, Zehuan Yuan, Xiatian Zhu |
| 2024 | Reconstruct and Match: Out-of-Distribution Robustness via Topological Homogeneity. Chaoqi Chen, Luyao Tang, Hui Huang |
| 2024 | Reconstructing the Image Stitching Pipeline: Integrating Fusion and Rectangling into a Unified Inpainting Model. Ziqi Xie, Weidong Zhao, Xianhui Liu, Jian Zhao, Ning Jia |
| 2024 | Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. Martín Bertrán, Shuai Tang, Michael Kearns, Jamie H. Morgenstern, Aaron Roth, Steven Z. Wu |
| 2024 | Reconstruction of Manipulated Garment with Guided Deformation Prior. Ren Li, Corentin Dumery, Zhantao Deng, Pascal Fua |
| 2024 | Recovering Complete Actions for Cross-dataset Skeleton Action Recognition. Hanchao Liu, Yujiang Li, Tai-Jiang Mu, Shi-Min Hu |
| 2024 | RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance. Zhicheng Sun, Zhenhao Yang, Yang Jin, Haozhe Chi, Kun Xu, Liwei Chen, Hao Jiang, Yang Song, Kun Gai, Yadong Mu |
| 2024 | Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery. Anand Gopalakrishnan, Aleksandar Stanic, Jürgen Schmidhuber, Michael C. Mozer |
| 2024 | Recurrent Reinforcement Learning with Memoroids. Steven D. Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob N. Foerster, Amanda Prorok |
| 2024 | Recurrent neural network dynamical systems for biological vision. Wayne Soo, Aldo Battista, Puria Radmard, Xiao-Jing Wang |
| 2024 | Recurrent neural networks: vanishing and exploding gradients are not the end of the story. Nicolas Zucchet, Antonio Orvieto |
| 2024 | Recursive Introspection: Teaching Language Model Agents How to Self-Improve. Yuxiao Qu, Tianjun Zhang, Naman Garg, Aviral Kumar |
| 2024 | Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss. Yi-Shan Wu, Yijie Zhang, Badr-Eddine Chérief-Abdellatif, Yevgeny Seldin |
| 2024 | RedCode: Risky Code Execution and Generation Benchmark for Code Agents. Chengquan Guo, Xun Liu, Chulin Xie, Andy Zhou, Yi Zeng, Zinan Lin, Dawn Song, Bo Li |
| 2024 | RedPajama: an Open Dataset for Training Large Language Models. Maurice Weber, Daniel Y. Fu, Quentin Anthony, Yonatan Oren, Shane Adams, Anton Alexandrov, Xiaozhong Lyu, Huu Nguyen, Xiaozhe Yao, Virginia Adams, Ben Athiwaratkun, Rahul Chalamala, Kezhen Chen, Max Ryabinin, Tri Dao, Percy Liang, Christopher Ré, Irina Rish, Ce Zhang |
| 2024 | Reducing Transformer Key-Value Cache Size with Cross-Layer Attention. William Brandon, Mayank Mishra, Aniruddha Nrusimha, Rameswar Panda, Jonathan Ragan-Kelley |
| 2024 | RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance. Jiaojiao Fan, Haotian Xue, Qinsheng Zhang, Yongxin Chen |
| 2024 | Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models. Luohe Shi, Yao Yao, Zuchao Li, Lefei Zhang, Hai Zhao |
| 2024 | Referencing Where to Focus: Improving Visual Grounding with Referential Query. Yabing Wang, Zhuotao Tian, Qingpei Guo, Zheng Qin, Sanping Zhou, Ming Yang, Le Wang |
| 2024 | Referring Human Pose and Mask Estimation In the Wild. Bo Miao, Mingtao Feng, Zijie Wu, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian |
| 2024 | Reflective Multi-Agent Collaboration based on Large Language Models. Xiaohe Bo, Zeyu Zhang, Quanyu Dai, Xueyang Feng, Lei Wang, Rui Li, Xu Chen, Ji-Rong Wen |
| 2024 | Refusal in Language Models Is Mediated by a Single Direction. Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Panickssery, Wes Gurnee, Neel Nanda |
| 2024 | RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks. Jiaxing Zhang, Zhuomin Chen, Hao Mei, Longchao Da, Dongsheng Luo, Hua Wei |
| 2024 | Regression under demographic parity constraints via unlabeled post-processing. Gayane Taturyan, Evgenii Chzhen, Mohamed Hebiri |
| 2024 | Regret Minimization in Stackelberg Games with Side Information. Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan |
| 2024 | Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network. Zih-Syuan Huang, Ching-pei Lee |
| 2024 | Regularized Conditional Diffusion Model for Multi-Task Preference Alignment. Xudong Yu, Chenjia Bai, Haoran He, Changhong Wang, Xuelong Li |
| 2024 | Regularized Q-Learning. Han-Dong Lim, Donghwan Lee |
| 2024 | Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs. Rui Yang, Ruomeng Ding, Yong Lin, Huan Zhang, Tong Zhang |
| 2024 | Reimagining Mutual Information for Enhanced Defense against Data Leakage in Collaborative Inference. Lin Duan, Jingwei Sun, Jinyuan Jia, Yiran Chen, Maria Gorlatova |
| 2024 | Reinforced Cross-Domain Knowledge Distillation on Time Series Data. Qing Xu, Min Wu, Xiaoli Li, Kezhi Mao, Zhenghua Chen |
| 2024 | Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers. Kai Yan, Alexander G. Schwing, Yu-Xiong Wang |
| 2024 | Reinforcement Learning Guided Semi-Supervised Learning. Marzi Heidari, Hanping Zhang, Yuhong Guo |
| 2024 | Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer. Ke Xue, Ruo-Tong Chen, Xi Lin, Yunqi Shi, Shixiong Kai, Siyuan Xu, Chao Qian |
| 2024 | Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity. Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi |
| 2024 | Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems. Haozhe Tian, Homayoun Hamedmoghadam, Robert Shorten, Pietro Ferraro |
| 2024 | Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control. Jinzhu Luo, Dingyang Chen, Qi Zhang |
| 2024 | Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards. Xuan-Bach Le, Dominik Wagner, Leon Witzman, Alexander Rabinovich, Luke Ong |
| 2024 | Reinforcement Learning with Lookahead Information. Nadav Merlis |
| 2024 | Reinforcing LLM Agents via Policy Optimization with Action Decomposition. Muning Wen, Ziyu Wan, Jun Wang, Weinan Zhang, Ying Wen |
| 2024 | Rejection via Learning Density Ratios. Alexander Soen, Hisham Husain, Philip Schulz, Vu Nguyen |
| 2024 | RelBench: A Benchmark for Deep Learning on Relational Databases. Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec |
| 2024 | Relating Hopfield Networks to Episodic Control. Hugo Chateau-Laurent, Frédéric Alexandre |
| 2024 | Relational Concept Bottleneck Models. Pietro Barbiero, Francesco Giannini, Gabriele Ciravegna, Michelangelo Diligenti, Giuseppe Marra |
| 2024 | Relational Verification Leaps Forward with RABBit. Tarun Suresh, Debangshu Banerjee, Gagandeep Singh |
| 2024 | Relationship Prompt Learning is Enough for Open-Vocabulary Semantic Segmentation. Jiahao Li, Yang Lu, Yuan Xie, Yanyun Qu |
| 2024 | Reliable Learning of Halfspaces under Gaussian Marginals. Ilias Diakonikolas, Lisheng Ren, Nikos Zarifis |
| 2024 | Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising. Gongfan Fang, Xinyin Ma, Xinchao Wang |
| 2024 | Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov |
| 2024 | Renovating Names in Open-Vocabulary Segmentation Benchmarks. Haiwen Huang, Songyou Peng, Dan Zhang, Andreas Geiger |
| 2024 | RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content. João Monteiro, Pierre-André Noël, Étienne Marcotte, Sai Rajeswar Mudumba, Valentina Zantedeschi, David Vázquez, Nicolas Chapados, Chris Pal, Perouz Taslakian |
| 2024 | Reparameterization invariance in approximate Bayesian inference. Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg |
| 2024 | Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth |
| 2024 | ReplaceAnything3D: Text-Guided Object Replacement in 3D Scenes with Compositional Scene Representations. Edward Bartrum, Thu Nguyen-Phuoc, Christopher Xie, Zhengqin Li, Numair Khan, Armen Avetisyan, Douglas Lanman, Lei Xiao |
| 2024 | Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach. Chaoxi Niu, Guansong Pang, Ling Chen, Bing Liu |
| 2024 | Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma. Jason Vander Woude, Peter Dixon, Aduri Pavan, Jamie Radcliffe, N. V. Vinodchandran |
| 2024 | Replicable Uniformity Testing. Sihan Liu, Christopher Ye |
| 2024 | Representation Noising: A Defence Mechanism Against Harmful Finetuning. Domenic Rosati, Jan Wehner, Kai Williams, Lukasz Bartoszcze, Robie Gonzales, Carsten Maple, Subhabrata Majumdar, Hassan Sajjad, Frank Rudzicz |
| 2024 | Reproducibility of predictive networks for mouse visual cortex. Polina Turishcheva, Max F. Burg, Fabian H. Sinz, Alexander S. Ecker |
| 2024 | Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design. Xiangxin Zhou, Jiaqi Guan, Yijia Zhang, Xingang Peng, Liang Wang, Jianzhu Ma |
| 2024 | Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe. Albert Q. Jiang, Alicja Ziarko, Bartosz Piotrowski, Wenda Li, Mateja Jamnik, Piotr Milos |
| 2024 | Reranking Laws for Language Generation: A Communication-Theoretic Perspective. António Farinhas, Haau-Sing Li, André F. T. Martins |
| 2024 | ResAD: A Simple Framework for Class Generalizable Anomaly Detection. Xincheng Yao, Zixin Chen, Chao Gao, Guangtao Zhai, Chongyang Zhang |
| 2024 | Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise. Zhenning Shi, Haoshuai Zheng, Chen Xu, Changsheng Dong, Bin Pan, Xueshuo Xie, Along He, Tao Li, Huazhu Fu |
| 2024 | Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization. Thomas Nagler, Lennart Schneider, Bernd Bischl, Matthias Feurer |
| 2024 | Resolving Discrepancies in Compute-Optimal Scaling of Language Models. Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, Yair Carmon |
| 2024 | Resource-Aware Federated Self-Supervised Learning with Global Class Representations. Mingyi Li, Xiao Zhang, Qi Wang, Tengfei Liu, Ruofan Wu, Weiqiang Wang, Fuzhen Zhuang, Hui Xiong, Dongxiao Yu |
| 2024 | RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models. Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Sixiang Chen, Tian Ye, Renjing Pei, Kaiwen Zhou, Fenglong Song, Lei Zhu |
| 2024 | Rethinking 3D Convolution in $\ell_p$-norm Space. Li Zhang, Yan Zhong, Jianan Wang, Zhe Min, RujingWang, Liu Liu |
| 2024 | Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective. Qishuai Wen, Chun-Guang Li |
| 2024 | Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints. Jay Bear, Adam Prügel-Bennett, Jonathon Hare |
| 2024 | Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus. Yiming Wang, Kaiyan Zhao, Furui Liu, Leong Hou U |
| 2024 | Rethinking Fourier Transform from A Basis Functions Perspective for Long-term Time Series Forecasting. Runze Yang, Longbing Cao, Jie Yang, Jianxun Li |
| 2024 | Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability, Reproducibility, and Practicality. Tianle Zhang, Langtian Ma, Yuchen Yan, Yuchen Zhang, Yue Yang, Ziyao Guo, Wenqi Shao, Kai Wang, Yang You, Yu Qiao, Ping Luo, Kaipeng Zhang |
| 2024 | Rethinking Imbalance in Image Super-Resolution for Efficient Inference. Wei Yu, Bowen Yang, Qinglin Liu, Jianing Li, Shengping Zhang, Xiangyang Ji |
| 2024 | Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment. Weichao Zhou, Wenchao Li |
| 2024 | Rethinking LLM Memorization through the Lens of Adversarial Compression. Avi Schwarzschild, Zhili Feng, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter |
| 2024 | Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models. Hanxiao Zhang, Lin Ju, Chan Wu, Jinjing Huang, Youshao Xiao, Zhenglei Zhou, Zhiming Fan, Zhaoxin Huan, Siyuan Li, Fanzhuang Meng, Lei Liang, Xiaolu Zhang, Jun Zhou |
| 2024 | Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective. Yanan Zhang, Jiangmeng Li, Lixiang Liu, Wenwen Qiang |
| 2024 | Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity. Guhao Feng, Han Zhong |
| 2024 | Rethinking No-reference Image Exposure Assessment from Holism to Pixel: Models, Datasets and Benchmarks. Shuai He, Shuntian Zheng, Anlong Ming, Banyu Wu, Huadong Ma |
| 2024 | Rethinking Optimal Transport in Offline Reinforcement Learning. Arip Asadulaev, Rostislav Korst, Aleksandr Korotin, Vage Egiazarian, Andrey Filchenkov, Evgeny Burnaev |
| 2024 | Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution. Kai Liu, Zhihang Fu, Sheng Jin, Chao Chen, Ze Chen, Rongxin Jiang, Fan Zhou, Yaowu Chen, Jieping Ye |
| 2024 | Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz. Ge Yan, Mengfei Ran, Ruocheng Wang, Kaisen Pan, Junchi Yan |
| 2024 | Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy. Sunwoo Kim, Soo Yong Lee, Fanchen Bu, Shinhwan Kang, Kyungho Kim, Jaemin Yoo, Kijung Shin |
| 2024 | Rethinking Score Distillation as a Bridge Between Image Distributions. David McAllister, Songwei Ge, Jia-Bin Huang, David Jacobs, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa |
| 2024 | Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation. Jiaxin Cheng, Zixu Zhao, Tong He, Tianjun Xiao, Zheng Zhang, Yicong Zhou |
| 2024 | Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis. Honglin Li, Yunlong Zhang, Pingyi Chen, Zhongyi Shui, Chenglu Zhu, Lin Yang |
| 2024 | Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models. Junjiao Tian, Chengyue Huang, Zsolt Kira |
| 2024 | Rethinking the Capacity of Graph Neural Networks for Branching Strategy. Ziang Chen, Jialin Liu, Xiaohan Chen, Xinshang Wang, Wotao Yin |
| 2024 | Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective. Zhichao Chen, Haoxuan Li, Fangyikang Wang, Odin Zhang, Hu Xu, Xiaoyu Jiang, Zhihuan Song, Hao Wang |
| 2024 | Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox. Xingming Long, Jie Zhang, Shiguang Shan, Xilin Chen |
| 2024 | Rethinking the Membrane Dynamics and Optimization Objectives of Spiking Neural Networks. Hangchi Shen, Qian Zheng, Huamin Wang, Gang Pan |
| 2024 | Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective. Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun, Zirui Zhuang, Jinming Wu, Jianxin Liao |
| 2024 | Retrieval & Fine-Tuning for In-Context Tabular Models. Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini |
| 2024 | Retrieval-Augmented Diffusion Models for Time Series Forecasting. Jingwei Liu, Ling Yang, Hongyan Li, Shenda Hong |
| 2024 | Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge. Heewoong Noh, Namkyeong Lee, Gyoung S. Na, Chanyoung Park |
| 2024 | Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos. Polina Turishcheva, Paul G. Fahey, Michaela Vystrcilová, Laura Hansel, Rachel Froebe, Kayla Ponder, Yongrong Qiu, Konstantin Willeke, Mohammad Bashiri, Ruslan Baikulov, Yu Zhu, Lei Ma, Shan Yu, Tiejun Huang, Bryan Li, Wolf De Wulf, Nina Kudryashova, Matthias H. Hennig, Nathalie Rochefort, Arno Onken, Eric Y. Wang, Zhiwei Ding, Andreas S. Tolias, Fabian H. Sinz, Alexander S. Ecker |
| 2024 | Return of Unconditional Generation: A Self-supervised Representation Generation Method. Tianhong Li, Dina Katabi, Kaiming He |
| 2024 | Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data. Zhilin Zhao, Longbing Cao, Xuhui Fan, Wei-Shi Zheng |
| 2024 | Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yian Ma, Tong Zhang |
| 2024 | Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference. Jiabao Ji, Yujian Liu, Yang Zhang, Gaowen Liu, Ramana Kompella, Sijia Liu, Shiyu Chang |
| 2024 | Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection. Hui Wei, Zhixiang Wang, Kewei Zhang, Jiaqi Hou, Yuanwei Liu, Hao Tang, Zheng Wang |
| 2024 | Revisiting Differentially Private ReLU Regression. Meng Ding, Mingxi Lei, Liyang Zhu, Shaowei Wang, Di Wang, Jinhui Xu |
| 2024 | Revisiting Ensembling in One-Shot Federated Learning. Youssef Allouah, Akash Balasaheb Dhasade, Rachid Guerraoui, Nirupam Gupta, Anne-Marie Kermarrec, Rafael Pinot, Rafael Pires, Rishi Sharma |
| 2024 | Revisiting Few-Shot Object Detection with Vision-Language Models. Anish Madan, Neehar Peri, Shu Kong, Deva Ramanan |
| 2024 | Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning. Abdulkadir Çelikkanat, Andrés R. Masegosa, Thomas Nielsen |
| 2024 | Revisiting Score Propagation in Graph Out-of-Distribution Detection. Longfei Ma, Yiyou Sun, Kaize Ding, Zemin Liu, Fei Wu |
| 2024 | Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective. Yujie Mo, Zhihe Lu, Runpeng Yu, Xiaofeng Zhu, Xinchao Wang |
| 2024 | Revisiting motion information for RGB-Event tracking with MOT philosophy. Tianlu Zhang, Kurt Debattista, Qiang Zhang, Guiguang Ding, Jungong Han |
| 2024 | Revisiting the Integration of Convolution and Attention for Vision Backbone. Lei Zhu, Xinjiang Wang, Wayne Zhang, Rynson W. H. Lau |
| 2024 | Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation. Meihan Liu, Zhen Zhang, Jiachen Tang, Jiajun Bu, Bingsheng He, Sheng Zhou |
| 2024 | Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation. Jiaan Luo, Feng Hong, Jiangchao Yao, Bo Han, Ya Zhang, Yanfeng Wang |
| 2024 | Reward Machines for Deep RL in Noisy and Uncertain Environments. Andrew C. Li, Zizhao Chen, Toryn Q. Klassen, Pashootan Vaezipoor, Rodrigo Toro Icarte, Sheila A. McIlraith |
| 2024 | Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy. Zhenyu Guan, Xiangyu Kong, Fangwei Zhong, Yizhou Wang |
| 2024 | Right this way: Can VLMs Guide Us to See More to Answer Questions? Li Liu, Diji Yang, Sijia Zhong, Kalyana Suma Sree Tholeti, Lei Ding, Yi Zhang, Leilani Gilpin |
| 2024 | Risk-Averse Fine-tuning of Large Language Models. Sapana Chaudhary, Ujwal Dinesha, Dileep Kalathil, Srinivas Shakkottai |
| 2024 | Risk-sensitive control as inference with Rényi divergence. Kaito Ito, Kenji Kashima |
| 2024 | RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions. Easton K. Huch, Jieru Shi, Madeline R. Abbott, Jessica R. Golbus, Alexander Moreno, Walter H. Dempsey |
| 2024 | RoPINN: Region Optimized Physics-Informed Neural Networks. Haixu Wu, Huakun Luo, Yuezhou Ma, Jianmin Wang, Mingsheng Long |
| 2024 | Road Network Representation Learning with the Third Law of Geography. Haicang Zhou, Weiming Huang, Yile Chen, Tiantian He, Gao Cong, Yew Soon Ong |
| 2024 | RobIR: Robust Inverse Rendering for High-Illumination Scenes. Ziyi Yang, Chenyanzhen, Xinyu Gao, Yazhen Yuan, Yu Wu, Xiaowei Zhou, Xiaogang Jin |
| 2024 | RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation. Jiaming Liu, Mengzhen Liu, Zhenyu Wang, Pengju An, Xiaoqi Li, Kaichen Zhou, Senqiao Yang, Renrui Zhang, Yandong Guo, Shanghang Zhang |
| 2024 | Robot Policy Learning with Temporal Optimal Transport Reward. Yuwei Fu, Haichao Zhang, Di Wu, Wei Xu, Benoit Boulet |
| 2024 | Robust Conformal Prediction Using Privileged Information. Shai Feldman, Yaniv Romano |
| 2024 | Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence. Ruiming Guo, Mouxing Yang, Yijie Lin, Xi Peng, Peng Hu |
| 2024 | Robust Fine-tuning of Zero-shot Models via Variance Reduction. Beier Zhu, Jiequan Cui, Hanwang Zhang |
| 2024 | Robust Gaussian Processes via Relevance Pursuit. Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy |
| 2024 | Robust Graph Neural Networks via Unbiased Aggregation. Zhichao Hou, Ruiqi Feng, Tyler Derr, Xiaorui Liu |
| 2024 | Robust Mixture Learning when Outliers Overwhelm Small Groups. Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel, Alexander Wolters, Gleb Novikov, Amartya Sanyal, David Steurer, Fanny Yang |
| 2024 | Robust Neural Contextual Bandit against Adversarial Corruptions. Yunzhe Qi, Yikun Ban, Arindam Banerjee, Jingrui He |
| 2024 | Robust Offline Active Learning on Graphs. Yuanchen Wu, Yubai Yuan |
| 2024 | Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks. Andy Zhou, Bo Li, Haohan Wang |
| 2024 | Robust Reinforcement Learning from Corrupted Human Feedback. Alexander Bukharin, Ilgee Hong, Haoming Jiang, Zichong Li, Qingru Zhang, Zixuan Zhang, Tuo Zhao |
| 2024 | Robust Reinforcement Learning with General Utility. Ziyi Chen, Yan Wen, Zhengmian Hu, Heng Huang |
| 2024 | Robust Sleep Staging over Incomplete Multimodal Physiological Signals via Contrastive Imagination. Qi Shen, Junchang Xin, Bing Tian Dai, Shudi Zhang, Zhiqiong Wang |
| 2024 | Robust Sparse Regression with Non-Isotropic Designs. Chih-Hung Liu, Gleb Novikov |
| 2024 | Robust and Faster Zeroth-Order Minimax Optimization: Complexity and Applications. Weixin An, Yuanyuan Liu, Fanhua Shang, Hongying Liu |
| 2024 | Robust group and simultaneous inferences for high-dimensional single index model. Weichao Yang, Hongwei Shi, Xu Guo, Changliang Zou |
| 2024 | Robustly overfitting latents for flexible neural image compression. Yura Perugachi-Diaz, Arwin Gansekoele, Sandjai Bhulai |
| 2024 | RoleAgent: Building, Interacting, and Benchmarking High-quality Role-Playing Agents from Scripts. Jiaheng Liu, Zehao Ni, Haoran Que, Tao Sun, Noah Wang, Jian Yang, Jiakai Wang, Hongcheng Guo, Zhongyuan Peng, Ge Zhang, Jiayi Tian, Xingyuan Bu, Ke Xu, Wenge Rong, Junran Peng, Zhaoxiang Zhang |
| 2024 | Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching. Fernando Moreno-Pino, Alvaro Arroyo, Harrison Waldon, Xiaowen Dong, Álvaro Cartea |
| 2024 | RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models. Shuhao Chen, Weisen Jiang, Baijiong Lin, James T. Kwok, Yu Zhang |
| 2024 | Rule Based Rewards for Language Model Safety. Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian Kivlichan, Molly Lin, Alex Beutel, John Schulman, Lilian Weng |
| 2024 | Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts. Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszar |
| 2024 | S Xinyu Yang, Jixuan Leng, Geyang Guo, Jiawei Zhao, Ryumei Nakada, Linjun Zhang, Huaxiu Yao, Beidi Chen |
| 2024 | S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search. Gengmo Zhou, Zhen Wang, Feng Yu, Guolin Ke, Zhewei Wei, Zhifeng Gao |
| 2024 | S-SOS: Stochastic Sum-Of-Squares for Parametric Polynomial Optimization. Licheng Zhu, Mathias Oster, Yuehaw Khoo |
| 2024 | S-STE: Continuous Pruning Function for Efficient 2: 4 Sparse Pre-training. Yuezhou Hu, Jun Zhu, Jianfei Chen |
| 2024 | S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning. Weihao Lin, Shengji Tang, Chong Yu, Peng Ye, Tao Chen |
| 2024 | SA3DIP: Segment Any 3D Instance with Potential 3D Priors. Xi Yang, Xu Gu, Xingyilang Yin, Xinbo Gao |
| 2024 | SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models. Linglan Zhao, Xuerui Zhang, Ke Yan, Shouhong Ding, Weiran Huang |
| 2024 | SAM-Guided Masked Token Prediction for 3D Scene Understanding. Zhimin Chen, Liang Yang, Yingwei Li, Longlong Jing, Bing Li |
| 2024 | SAMPa: Sharpness-aware Minimization Parallelized. Wanyun Xie, Thomas Pethick, Volkan Cevher |
| 2024 | SAND: Smooth imputation of sparse and noisy functional data with Transformer networks. Ju-Sheng Hong, Junwen Yao, Jonas W. Mueller, Jane-Ling Wang |
| 2024 | SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series. Zhihao Dai, Ligang He, Shuanghua Yang, Matthew Leeke |
| 2024 | SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection. Yuxuan Li, Xiang Li, Weijie Li, Qibin Hou, Li Liu, Ming-Ming Cheng, Jian Yang |
| 2024 | SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines |
| 2024 | SCOREQ: Speech Quality Assessment with Contrastive Regression. Alessandro Ragano, Jan Skoglund, Andrew Hines |
| 2024 | SCRREAM : SCan, Register, REnder And Map: A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark. HyunJun Jung, Weihang Li, Shun-Cheng Wu, William Bittner, Nikolas Brasch, Jifei Song, Eduardo Pérez-Pellitero, Zhensong Zhang, Arthur Moreau, Nassir Navab, Benjamin Busam |
| 2024 | SCaR: Refining Skill Chaining for Long-Horizon Robotic Manipulation via Dual Regularization. Zixuan Chen, Ze Ji, Jing Huo, Yang Gao |
| 2024 | SCube: Instant Large-Scale Scene Reconstruction using VoxSplats. Xuanchi Ren, Yifan Lu, Hanxue Liang, Jay Zhangjie Wu, Huan Ling, Mike Chen, Sanja Fidler, Francis Williams, Jiahui Huang |
| 2024 | SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words. Junyi Ao, Yuancheng Wang, Xiaohai Tian, Dekun Chen, Jun Zhang, Lu Lu, Yuxuan Wang, Haizhou Li, Zhizheng Wu |
| 2024 | SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training. Jinda Jia, Cong Xie, Hanlin Lu, Daoce Wang, Hao Feng, Chengming Zhang, Baixi Sun, Haibin Lin, Zhi Zhang, Xin Liu, Dingwen Tao |
| 2024 | SDformer: Similarity-driven Discrete Transformer For Time Series Generation. Zhicheng Chen, Shibo Feng, Zhong Zhang, Xi Xiao, Xingyu Gao, Peilin Zhao |
| 2024 | SE(3)-bi-equivariant Transformers for Point Cloud Assembly. Ziming Wang, Rebecka Jörnsten |
| 2024 | SEA: State-Exchange Attention for High-Fidelity Physics Based Transformers. Parsa Esmati, Amirhossein Dadashzadeh, Vahid Ardakani, Nicolas Larrosa, Nicolò Grilli |
| 2024 | SEEV: Synthesis with Efficient Exact Verification for ReLU Neural Barrier Functions. Hongchao Zhang, Zhizhen Qin, Sicun Gao, Andrew Clark |
| 2024 | SEL-BALD: Deep Bayesian Active Learning with Selective Labels. Ruijiang Gao, Mingzhang Yin, Maytal Saar-Tsechansky |
| 2024 | SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification. Benjamin Feuer, Jiawei Xu, Niv Cohen, Patrick Yubeaton, Govind Mittal, Chinmay Hegde |
| 2024 | SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures. Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng |
| 2024 | SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data. Jialu Li, Jaemin Cho, Yi-Lin Sung, Jaehong Yoon, Mohit Bansal |
| 2024 | SETLEXSEM CHALLENGE: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language Models. Nicholas A. Dronen, Bardiya Akhbari, Manish Gawali |
| 2024 | SF-V: Single Forward Video Generation Model. Zhixing Zhang, Yanyu Li, Yushu Wu, Yanwu Xu, Anil Kag, Ivan Skorokhodov, Willi Menapace, Aliaksandr Siarohin, Junli Cao, Dimitris N. Metaxas, Sergey Tulyakov, Jian Ren |
| 2024 | SG-Bench: Evaluating LLM Safety Generalization Across Diverse Tasks and Prompt Types. Yutao Mou, Shikun Zhang, Wei Ye |
| 2024 | SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation. Hang Yin, Xiuwei Xu, Zhenyu Wu, Jie Zhou, Jiwen Lu |
| 2024 | SGD vs GD: Rank Deficiency in Linear Networks. Aditya Vardhan Varre, Margarita Sagitova, Nicolas Flammarion |
| 2024 | SGLang: Efficient Execution of Structured Language Model Programs. Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark W. Barrett, Ying Sheng |
| 2024 | SHDocs: A dataset, benchmark, and method to efficiently generate high-quality, real-world specular highlight data with near-perfect alignment. Jovin Leong, Koa Di, Benjamin Cham, Shaun Heng |
| 2024 | SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning. Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li |
| 2024 | SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models. Zhaoyang Sun, Shengwu Xiong, Yaxiong Chen, Fei Du, Weihua Chen, Fan Wang, Yi Rong |
| 2024 | SILENCE: Protecting privacy in offloaded speech understanding on resource-constrained devices. Dongqi Cai, Shangguang Wang, Zeling Zhang, Felix Xiaozhu Lin, Mengwei Xu |
| 2024 | SIRIUS : Contexual Sparisty with Correction for Efficient LLMs. Yang Zhou, Zhuoming Chen, Zhaozhuo Xu, Victoria Lin, Beidi Chen |
| 2024 | SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models. Jianyi Zhang, Da-Cheng Juan, Cyrus Rashtchian, Chun-Sung Ferng, Heinrich Jiang, Yiran Chen |
| 2024 | SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection. Yi Zhu, Surya Koppisetti, Trang Tran, Gaurav Bharaj |
| 2024 | SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining. Andi Han, Jiaxiang Li, Wei Huang, Mingyi Hong, Akiko Takeda, Pratik Kumar Jawanpuria, Bamdev Mishra |
| 2024 | SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. Tehila Dahan, Kfir Y. Levy |
| 2024 | SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark. Sithursan Sivasubramaniam, Cedric Osei-Akoto, Yi Zhang, Kurt Stockinger, Jonathan Fürst |
| 2024 | SMART: Scalable Multi-agent Real-time Motion Generation via Next-token Prediction. Wei Wu, Xiaoxin Feng, Ziyan Gao, Yuheng Kan |
| 2024 | SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction. Zhihao Yu, Chu Xu, Yujie Jin, Yasha Wang, Junfeng Zhao |
| 2024 | SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion. Han Lu, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan |
| 2024 | SOI: Scaling Down Computational Complexity by Estimating Partial States of the Model. Grzegorz Stefanski, Pawel Daniluk, Artur Szumaczuk, Jakub Tkaczuk |
| 2024 | SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization. Shuchen Zhu, Boao Kong, Songtao Lu, Xinmeng Huang, Kun Yuan |
| 2024 | SPEAR: Exact Gradient Inversion of Batches in Federated Learning. Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin T. Vechev |
| 2024 | SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers. Shraman Pramanick, Rama Chellappa, Subhashini Venugopalan |
| 2024 | SPO: Sequential Monte Carlo Policy Optimisation. Matthew Macfarlane, Edan Toledo, Donal Byrne, Paul Duckworth, Alexandre Laterre |
| 2024 | SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning. Huy Hoang, Tien Mai, Pradeep Varakantham |
| 2024 | SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution. Soufiane Belharbi, Mara KM Whitford, Phuong Hoang, Shakeeb Murtaza, Luke McCaffrey, Eric Granger |
| 2024 | SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding. Jiefeng Ma, Yan Wang, Chenyu Liu, Jun Du, Yu Hu, Zhenrong Zhang, Pengfei Hu, Qing Wang, Jianshu Zhang |
| 2024 | SS1: Accelerating Inference with Fast and Expressive Sketch Structured Transform. Aditya Desai, Kimia Saedi, Apoorv Walia, Jihyeong Lee, Keren Zhou, Anshumali Shrivastava |
| 2024 | SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset. Yubin Hu, Kairui Wen, Heng Zhou, Xiaoyang Guo, Yong-Jin Liu |
| 2024 | SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation. Xiaowen Ma, Zhenliang Ni, Xinghao Chen |
| 2024 | SSDM: Scalable Speech Dysfluency Modeling. Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Baquirin, Zachary A. Miller, Maria Luisa Gorno-Tempini, Gopala Anumanchipalli |
| 2024 | SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening. Yu Zhong, Xiao Wu, Liang-Jian Deng, Zihan Cao, Hong-Xia Dou |
| 2024 | ST$_k$: A Scalable Module for Solving Top-k Problems. Hanchen Xia, Weidong Liu, Xiaojun Mao |
| 2024 | START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation. Jintao Guo, Lei Qi, Yinghuan Shi, Yang Gao |
| 2024 | STL: Still Tricky Logic (for System Validation, Even When Showing Your Work). Isabelle Hurley, Rohan Paleja, Ashley Suh, Jaime Daniel Peña, Ho Chit Siu |
| 2024 | STONE: A Submodular Optimization Framework for Active 3D Object Detection. Ruiyu Mao, Sarthak Kumar Maharana, Rishabh K. Iyer, Yunhui Guo |
| 2024 | STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases. Shirley Wu, Shiyu Zhao, Michihiro Yasunaga, Kexin Huang, Kaidi Cao, Qian Huang, Vassilis N. Ioannidis, Karthik Subbian, James Y. Zou, Jure Leskovec |
| 2024 | STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics. Jiawen Chen, Muqing Zhou, Wenrong Wu, Jinwei Zhang, Yun Li, Didong Li |
| 2024 | SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations. Sri Harsha Dumpala, Aman Jaiswal, Chandramouli Shama Sastry, Evangelos E. Milios, Sageev Oore, Hassan Sajjad |
| 2024 | SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors. Vijay Lingam, Atula Neerkaje, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Eunsol Choi, Alex Dimakis, Aleksandar Bojchevski, Sujay Sanghavi |
| 2024 | SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering. John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, Ofir Press |
| 2024 | SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents. Niels Mündler, Mark Niklas Müller, Jingxuan He, Martin T. Vechev |
| 2024 | Safe Exploitative Play with Untrusted Type Beliefs. Tongxin Li, Tinashe Handina, Shaolei Ren, Adam Wierman |
| 2024 | Safe LoRA: The Silver Lining of Reducing Safety Risks when Finetuning Large Language Models. Chia-Yi Hsu, Yu-Lin Tsai, Chih-Hsun Lin, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang |
| 2024 | Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel. Jialin Li, Marta Zagórowska, Giulia De Pasquale, Alisa Rupenyan, John Lygeros |
| 2024 | Safe and Efficient: A Primal-Dual Method for Offline Convex CMDPs under Partial Data Coverage. Haobo Zhang, Xiyue Peng, Honghao Wei, Xin Liu |
| 2024 | Safe and Sparse Newton Method for Entropic-Regularized Optimal Transport. Zihao Tang, Yixuan Qiu |
| 2024 | SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset. Juntao Dai, Tianle Chen, Xuyao Wang, Ziran Yang, Taiye Chen, Jiaming Ji, Yaodong Yang |
| 2024 | SafeWorld: Geo-Diverse Safety Alignment. Da Yin, Haoyi Qiu, Kung-Hsiang Huang, Kai-Wei Chang, Nanyun Peng |
| 2024 | Safety through feedback in Constrained RL. Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri |
| 2024 | Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? Richard Ren, Steven Basart, Adam Khoja, Alice Gatti, Long Phan, Xuwang Yin, Mantas Mazeika, Alexander Pan, Gabriel Mukobi, Ryan H. Kim, Stephen Fitz, Dan Hendrycks |
| 2024 | Saliency-driven Experience Replay for Continual Learning. Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara, Simone Palazzo, Concetto Spampinato |
| 2024 | Samba: Severity-aware Recurrent Modeling for Cross-domain Medical Image Grading. Qi Bi, Jingjun Yi, Hao Zheng, Wei Ji, Haolan Zhan, Yawen Huang, Yuexiang Li, Yefeng Zheng |
| 2024 | SampDetox: Black-box Backdoor Defense via Perturbation-based Sample Detoxification. Yanxin Yang, Chentao Jia, Dengke Yan, Ming Hu, Tianlin Li, Xiaofei Xie, Xian Wei, Mingsong Chen |
| 2024 | Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning. Adhyyan Narang, Andrew Wagenmaker, Lillian J. Ratliff, Kevin Jamieson |
| 2024 | Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut. Hongyu Cheng, Sammy Khalife, Barbara Fiedorowicz, Amitabh Basu |
| 2024 | Sample Complexity of Interventional Causal Representation Learning. Emre Acartürk, Burak Varici, Karthikeyan Shanmugam, Ali Tajer |
| 2024 | Sample Complexity of Posted Pricing for a Single Item. Billy Jin, Thomas Kesselheim, Will Ma, Sahil Singla |
| 2024 | Sample Efficient Bayesian Learning of Causal Graphs from Interventions. Zihan Zhou, Muhammad Qasim Elahi, Murat Kocaoglu |
| 2024 | Sample Selection via Contrastive Fragmentation for Noisy Label Regression. Chris Dongjoo Kim, Sangwoo Moon, Jihwan Moon, Dongyeon Woo, Gunhee Kim |
| 2024 | Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models. Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas |
| 2024 | Sample-Efficient Agnostic Boosting. Udaya Ghai, Karan Singh |
| 2024 | Sample-Efficient Constrained Reinforcement Learning with General Parameterization. Washim Uddin Mondal, Vaneet Aggarwal |
| 2024 | Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization. Ipsita Ghosh, Abiy Tasissa, Christian Kümmerle |
| 2024 | Sample-Efficient Private Learning of Mixtures of Gaussians. Hassan Ashtiani, Mahbod Majid, Shyam Narayanan |
| 2024 | Sample-efficient Bayesian Optimisation Using Known Invariances. Theodore Brown, Alexandru Cioba, Ilija Bogunovic |
| 2024 | Satformer: Accurate and Robust Traffic Data Estimation for Satellite Networks. Liang Qin, Xiyuan Liu, Wenting Wei, Chengbin Liang, Huaxi Gu |
| 2024 | SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain. Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, Rui Melo, Gabriel Hautreux, Etienne Malaboeuf, Johanne Charpentier, Dominic Culver, Michael Desa |
| 2024 | Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes. Yunyue Wei, Vincent Zhuang, Saraswati Soedarmadji, Yanan Sui |
| 2024 | Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning. Lijun Zhang, Lin Li, Wei Wei, Huizhong Song, Yaodong Yang, Jiye Liang |
| 2024 | Scalable DBSCAN with Random Projections. Haochuan Xu, Ninh Pham |
| 2024 | Scalable DP-SGD: Shuffling vs. Poisson Subsampling. Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang |
| 2024 | Scalable Early Childhood Reading Performance Prediction. Zhongkai Shangguan, Zanming Huang, Eshed Ohn-Bar, Ola Ozernov-Palchik, Derek Kosty, Michael Stoolmiller, Hank Fien |
| 2024 | Scalable Kernel Inverse Optimization. Youyuan Long, Tolga Ok, Pedro Zattoni Scroccaro, Peyman Mohajerin Esfahani |
| 2024 | Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes. Duo Zhou, Christopher Brix, Grani A. Hanasusanto, Huan Zhang |
| 2024 | Scalable Optimization in the Modular Norm. Tim Large, Yang Liu, Jacob Huh, Hyojin Bahng, Phillip Isola, Jeremy Bernstein |
| 2024 | Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs. Yao Lai, Jinxin Liu, David Z. Pan, Ping Luo |
| 2024 | Scale Equivariant Graph Metanetworks. Ioannis Kalogeropoulos, Giorgos Bouritsas, Yannis Panagakis |
| 2024 | Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models. Jing Wang, HaiYing Wang, Hao Zhang |
| 2024 | ScaleKD: Strong Vision Transformers Could Be Excellent Teachers. Jiawei Fan, Chao Li, Xiaolong Liu, Anbang Yao |
| 2024 | Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits. Gennaro Gala, Cassio P. de Campos, Antonio Vergari, Erik Quaeghebeur |
| 2024 | Scaling Law for Time Series Forecasting. Jingzhe Shi, Qinwei Ma, Huan Ma, Lei Li |
| 2024 | Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations. Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Ben Allal, Leandro von Werra, Martin Jaggi |
| 2024 | Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms. Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. Bradley Knox, Chelsea Finn, Scott Niekum |
| 2024 | Scaling Laws in Linear Regression: Compute, Parameters, and Data. Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee |
| 2024 | Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies. Chaofan Tao, Qian Liu, Longxu Dou, Niklas Muennighoff, Zhongwei Wan, Ping Luo, Min Lin, Ngai Wong |
| 2024 | Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers. Lirui Wang, Xinlei Chen, Jialiang Zhao, Kaiming He |
| 2024 | Scaling Retrieval-Based Language Models with a Trillion-Token Datastore. Rulin Shao, Jacqueline He, Akari Asai, Weijia Shi, Tim Dettmers, Sewon Min, Luke Zettlemoyer, Pang Wei Koh |
| 2024 | Scaling Sign Language Translation. Biao Zhang, Garrett Tanzer, Orhan Firat |
| 2024 | Scaling White-Box Transformers for Vision. Jinrui Yang, Xianhang Li, Druv Pai, Yuyin Zhou, Yi Ma, Yaodong Yu, Cihang Xie |
| 2024 | Scaling laws for learning with real and surrogate data. Ayush Jain, Andrea Montanari, Eren Sasoglu |
| 2024 | Scaling the Codebook Size of VQ-GAN to 100, 000 with a Utilization Rate of 99%. Lei Zhu, Fangyun Wei, Yanye Lu, Dong Chen |
| 2024 | Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Romit Maulik, Rao Kotamarthi, Ian T. Foster, Sandeep Madireddy, Aditya Grover |
| 2024 | Scanning Trojaned Models Using Out-of-Distribution Samples. Hossein Mirzaei, Ali Ansari, Bahar Dibaei Nia, Mojtaba Nafez, Moein Madadi, Sepehr Rezaee, Zeinab Taghavi, Arad Maleki, Kian Shamsaie, Mahdi Hajialilue, Jafar Habibi, Mohammad Sabokrou, Mohammad Hossein Rohban |
| 2024 | Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation. Yunnan Wang, Ziqiang Li, Wenyao Zhang, Zequn Zhang, Baao Xie, Xihui Liu, Wenjun Zeng, Xin Jin |
| 2024 | Scene Graph Generation with Role-Playing Large Language Models. Guikun Chen, Jin Li, Wenguan Wang |
| 2024 | SceneCraft: Layout-Guided 3D Scene Generation. Xiuyu Yang, Yunze Man, Jun-Kun Chen, Yu-Xiong Wang |
| 2024 | SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout. Chiyu Max Jiang, Yijing Bai, Andre Cornman, Christopher Davis, Xiukun Huang, Hong Jeon, Sakshum Kulshrestha, John Lambert, Shuangyu Li, Xuanyu Zhou, Carlos Fuertes, Chang Yuan, Mingxing Tan, Yin Zhou, Dragomir Anguelov |
| 2024 | Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing. Haonan Lin, Yan Chen, Jiahao Wang, Wenbin An, Mengmeng Wang, Feng Tian, Yong Liu, Guang Dai, Jingdong Wang, Qianying Wang |
| 2024 | Schrodinger Bridge Flow for Unpaired Data Translation. Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet |
| 2024 | Schur Nets: exploiting local structure for equivariance in higher order graph neural networks. Qingqi Zhang, Ruize Xu, Risi Kondor |
| 2024 | SciCode: A Research Coding Benchmark Curated by Scientists. Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang, Xinan Chen, Cunwei Fan, Xuefei Guo, Roland Haas, Pan Ji, Kittithat Krongchon, Yao Li, Shengyan Liu, Di Luo, Yutao Ma, Hao Tong, Kha Trinh, Chenyu Tian, Zihan Wang, Bohao Wu, Shengzhu Yin, Minhui Zhu, Kilian Lieret, Yanxin Lu, Genglin Liu, Yufeng Du, Tianhua Tao, Ofir Press, Jamie Callan, Eliu A. Huerta, Hao Peng |
| 2024 | SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation. Jonathan Roberts, Kai Han, Neil Houlsby, Samuel Albanie |
| 2024 | SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models. Dan Zhang, Ziniu Hu, Sining Zhoubian, Zhengxiao Du, Kaiyu Yang, Zihan Wang, Yisong Yue, Yuxiao Dong, Jie Tang |
| 2024 | Score Distillation via Reparametrized DDIM. Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan H. Greenewald, Vitor Guizilini, Timur M. Bagautdinov, Vincent Sitzmann, Justin M. Solomon |
| 2024 | Score-Optimal Diffusion Schedules. Christopher Williams, Andrew Campbell, Arnaud Doucet, Saifuddin Syed |
| 2024 | Score-based 3D molecule generation with neural fields. Matthieu Kirchmeyer, Pedro O. Pinheiro, Saeed Saremi |
| 2024 | Score-based generative models are provably robust: an uncertainty quantification perspective. Nikiforos Mimikos-Stamatopoulos, Benjamin J. Zhang, Markos A. Katsoulakis |
| 2024 | Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets. Wolfgang Boettcher, Lukas Hoyer, Ozan Unal, Jan Eric Lenssen, Bernt Schiele |
| 2024 | SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation. Yixia Li, Boya Xiong, Guanhua Chen, Yun Chen |
| 2024 | SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey. Kien X. Nguyen, Fengchun Qiao, Arthur Trembanis, Xi Peng |
| 2024 | Search for Efficient Large Language Models. Xuan Shen, Pu Zhao, Yifan Gong, Zhenglun Kong, Zheng Zhan, Yushu Wu, Ming Lin, Chao Wu, Xue Lin, Yanzhi Wang |
| 2024 | SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge. Chuanhao Li, Zhen Li, Chenchen Jing, Shuo Liu, Wenqi Shao, Yuwei Wu, Ping Luo, Yu Qiao, Kaipeng Zhang |
| 2024 | Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Charlie Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson |
| 2024 | Second-order forward-mode optimization of recurrent neural networks for neuroscience. Youjing Yu, Rui Xia, Qingxi Ma, Máté Lengyel, Guillaume Hennequin |
| 2024 | Secret Collusion among AI Agents: Multi-Agent Deception via Steganography. Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier, Vijay Bolina, Philip Torr, Lewis Hammond, Christian Schröder de Witt |
| 2024 | SeeA*: Efficient Exploration-Enhanced A* Search by Selective Sampling. Dengwei Zhao, Shikui Tu, Lei Xu |
| 2024 | SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution. Qi Tang, Yao Zhao, Meiqin Liu, Chao Yao |
| 2024 | Seeing Beyond the Crop: Using Language Priors for Out-of-Bounding Box Keypoint Prediction. Bavesh Balaji, Jerrin Bright, Yuhao Chen, Sirisha Rambhatla, John S. Zelek, David A. Clausi |
| 2024 | Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment. Xin Xiao, Bohong Wu, Jiacong Wang, Chunyuan Li, Xun Zhou, Haoyuan Guo |
| 2024 | Seek Commonality but Preserve Differences: Dissected Dynamics Modeling for Multi-modal Visual RL. Yangru Huang, Peixi Peng, Yifan Zhao, Guangyao Chen, Yonghong Tian |
| 2024 | SegVol: Universal and Interactive Volumetric Medical Image Segmentation. Yuxin Du, Fan Bai, Tiejun Huang, Bo Zhao |
| 2024 | Segment Any Change. Zhuo Zheng, Yanfei Zhong, Liangpei Zhang, Stefano Ermon |
| 2024 | Segment Anything without Supervision. Xudong Wang, Jingfeng Yang, Trevor Darrell |
| 2024 | Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations. Shivam Grover, Amin Jalali, Ali Etemad |
| 2024 | Segmenting Watermarked Texts From Language Models. Xingchi Li, Guanxun Li, Xianyang Zhang |
| 2024 | SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection. Liangxin Liu, Xuebo Liu, Derek F. Wong, Dongfang Li, Ziyi Wang, Baotian Hu, Min Zhang |
| 2024 | Selective Attention: Enhancing Transformer through Principled Context Control. Xuechen Zhang, Xiangyu Chang, Mingchen Li, Amit K. Roy-Chowdhury, Jiasi Chen, Samet Oymak |
| 2024 | Selective Explanations. Lucas Monteiro Paes, Dennis Wei, Flávio P. Calmon |
| 2024 | Selective Generation for Controllable Language Models. Minjae Lee, Kyungmin Kim, Taesoo Kim, Sangdon Park |
| 2024 | Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection. Geng Yu, Jianing Zhu, Jiangchao Yao, Bo Han |
| 2024 | Self-Calibrating Conformal Prediction. Lars van der Laan, Ahmed M. Alaa |
| 2024 | Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences. Damien Ferbach, Quentin Bertrand, Avishek Joey Bose, Gauthier Gidel |
| 2024 | Self-Distilled Depth Refinement with Noisy Poisson Fusion. Jiaqi Li, Yiran Wang, Jinghong Zheng, Zihao Huang, Ke Xian, Zhiguo Cao, Jianming Zhang |
| 2024 | Self-Guided Masked Autoencoder. Jeongwoo Shin, Inseo Lee, Junho Lee, Joonseok Lee |
| 2024 | Self-Guiding Exploration for Combinatorial Problems. Zangir Iklassov, Yali Du, Farkhad Akimov, Martin Takác |
| 2024 | Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments. Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar |
| 2024 | Self-Labeling the Job Shop Scheduling Problem. Andrea Corsini, Angelo Porrello, Simone Calderara, Mauro Dell'Amico |
| 2024 | Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation. Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu |
| 2024 | Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations. Nikil Roashan Selvam, Amil Merchant, Stefano Ermon |
| 2024 | Self-Retrieval: End-to-End Information Retrieval with One Large Language Model. Qiaoyu Tang, Jiawei Chen, Zhuoqun Li, Bowen Yu, Yaojie Lu, Cheng Fu, Haiyang Yu, Hongyu Lin, Fei Huang, Ben He, Xianpei Han, Le Sun, Yongbin Li |
| 2024 | Self-Supervised Adversarial Training via Diverse Augmented Queries and Self-Supervised Double Perturbation. Ruize Zhang, Sheng Tang, Juan Cao |
| 2024 | Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels. Jan-Philipp Fränken, Eric Zelikman, Rafael Rafailov, Kanishk Gandhi, Tobias Gerstenberg, Noah D. Goodman |
| 2024 | Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models. Yuchen Hu, Chen Chen, Chao-Han Yang, Chengwei Qin, Pin-Yu Chen, Engsiong Chng, Chao Zhang |
| 2024 | Self-playing Adversarial Language Game Enhances LLM Reasoning. Pengyu Cheng, Tianhao Hu, Han Xu, Zhisong Zhang, Yong Dai, Lei Han, Nan Du, Xiaolong Li |
| 2024 | Self-supervised Transformation Learning for Equivariant Representations. Jaemyung Yu, Jaehyun Choi, Dong-Jae Lee, Hyeong Gwon Hong, Junmo Kim |
| 2024 | SelfCodeAlign: Self-Alignment for Code Generation. Yuxiang Wei, Federico Cassano, Jiawei Liu, Yifeng Ding, Naman Jain, Zachary Mueller, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang |
| 2024 | SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning. Yangruibo Ding, Jinjun Peng, Marcus J. Min, Gail E. Kaiser, Junfeng Yang, Baishakhi Ray |
| 2024 | SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow. Chaoyang Wang, Xiangtai Li, Lu Qi, Henghui Ding, Yunhai Tong, Ming-Hsuan Yang |
| 2024 | Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space. Xin Qiu, Risto Miikkulainen |
| 2024 | Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval. Lixu Wang, Xinyu Du, Qi Zhu |
| 2024 | Semantic Routing via Autoregressive Modeling. Eric Zhao, Pranjal Awasthi, Zhengdao Chen, Sreenivas Gollapudi, Daniel Delling |
| 2024 | Semantics and Spatiality of Emergent Communication. Rotem Ben Zion, Boaz Carmeli, Orr Paradise, Yonatan Belinkov |
| 2024 | Semi-Open 3D Object Retrieval via Hierarchical Equilibrium on Hypergraph. Yang Xu, Yifan Feng, Jun Zhang, Jun-Hai Yong, Yue Gao |
| 2024 | Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting. Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian |
| 2024 | Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data. Eyar Azar, Boaz Nadler |
| 2024 | Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors. Anisha Pal, Julia Kruk, Mansi Phute, Manognya Bhattaram, Diyi Yang, Duen Horng Chau, Judy Hoffman |
| 2024 | Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data. Fan Zhang, Tianyu Liu, Zihao Chen, Xiaojiang Peng, Chong Chen, Xian-Sheng Hua, Xiao Luo, Hongyu Zhao |
| 2024 | Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss. Ximing Li, Silong Liang, Changchun Li, Pengfei Wang, Fangming Gu |
| 2024 | Semidefinite Relaxations of the Gromov-Wasserstein Distance. Junyu Chen, Binh T. Nguyen, Shang Koh, Yong Sheng Soh |
| 2024 | Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation. Ui-Hyeop Shin, Sangyoun Lee, Taehan Kim, Hyung-Min Park |
| 2024 | Separation and Bias of Deep Equilibrium Models on Expressivity and Learning Dynamics. Zhoutong Wu, Yimu Zhang, Cong Fang, Zhouchen Lin |
| 2024 | Separations in the Representational Capabilities of Transformers and Recurrent Architectures. Satwik Bhattamishra, Michael Hahn, Phil Blunsom, Varun Kanade |
| 2024 | Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation. Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose |
| 2024 | Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity. Vahid Balazadeh Meresht, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis |
| 2024 | Sequential Harmful Shift Detection Without Labels. Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lécué, Daniele Magazzeni, Manuela Veloso |
| 2024 | Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood. Ziyi Liu, Idan Attias, Dan Roy |
| 2024 | Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks. Mitchell Keren Taraday, Almog David, Chaim Baskin |
| 2024 | SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization. Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, Mohammad Hossein Bateni, Vahab Mirrokni |
| 2024 | Sequoia: Scalable and Robust Speculative Decoding. Zhuoming Chen, Avner May, Ruslan Svirschevski, Yuhsun Huang, Max Ryabinin, Zhihao Jia, Beidi Chen |
| 2024 | Set-based Neural Network Encoding Without Weight Tying. Bruno Andreis, Bedionita Soro, Philip H. S. Torr, Sung Ju Hwang |
| 2024 | SfPUEL: Shape from Polarization under Unknown Environment Light. Youwei Lyu, Heng Guo, Kailong Zhang, Si Li, Boxin Shi |
| 2024 | Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models. Yuancheng Xu, Jiarui Yao, Manli Shu, Yanchao Sun, Zichu Wu, Ning Yu, Tom Goldstein, Furong Huang |
| 2024 | Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity. Alexander Tyurin, Marta Pozzi, Ivan Ilin, Peter Richtárik |
| 2024 | Shape analysis for time series. Thibaut Germain, Samuel Gruffaz, Charles Truong, Alain Durmus, Laurent Oudre |
| 2024 | Shaping the distribution of neural responses with interneurons in a recurrent circuit model. David Lipshutz, Eero P. Simoncelli |
| 2024 | ShareGPT4Video: Improving Video Understanding and Generation with Better Captions. Lin Chen, Xilin Wei, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Lin Bin, Zhenyu Tang, Li Yuan, Yu Qiao, Dahua Lin, Feng Zhao, Jiaqi Wang |
| 2024 | Shared Autonomy with IDA: Interventional Diffusion Assistance. Brandon McMahan, Zhenghao Mark Peng, Bolei Zhou, Jonathan C. Kao |
| 2024 | Sharing Key Semantics in Transformer Makes Efficient Image Restoration. Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita Cucchiara, Luc Van Gool, Ming-Hsuan Yang, Nicu Sebe |
| 2024 | Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization. Mingwei Xu, Xiaofeng Cao, Ivor W. Tsang |
| 2024 | Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance. Haiquan Lu, Xiaotian Liu, Yefan Zhou, Qunli Li, Kurt Keutzer, Michael W. Mahoney, Yujun Yan, Huanrui Yang, Yaoqing Yang |
| 2024 | Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin |
| 2024 | ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization. Haoran You, Yipin Guo, Yichao Fu, Wei Zhou, Huihong Shi, Xiaofan Zhang, Souvik Kundu, Amir Yazdanbakhsh, Yingyan (Celine) Lin |
| 2024 | Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models. Yilun Jin, Zheng Li, Chenwei Zhang, Tianyu Cao, Yifan Gao, Pratik Jayarao, Mao Li, Xin Liu, Ritesh Sarkhel, Xianfeng Tang, Haodong Wang, Zhengyang Wang, Wenju Xu, Jingfeng Yang, Qingyu Yin, Xian Li, Priyanka Nigam, Yi Xu, Kai Chen, Qiang Yang, Meng Jiang, Bing Yin |
| 2024 | Should We Really Edit Language Models? On the Evaluation of Edited Language Models. Qi Li, Xiang Liu, Zhenheng Tang, Peijie Dong, Zeyu Li, Xinglin Pan, Xiaowen Chu |
| 2024 | ShowMaker: Creating High-Fidelity 2D Human Video via Fine-Grained Diffusion Modeling. Quanwei Yang, Jiazhi Guan, Kaisiyuan Wang, Lingyun Yu, Wenqing Chu, Hang Zhou, Zhiqiang Feng, Haocheng Feng, Errui Ding, Jingdong Wang, Hongtao Xie |
| 2024 | Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization. Quoc Tran-Dinh, Trang H. Tran, Lam M. Nguyen |
| 2024 | Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts. Huy Nguyen, Nhat Ho, Alessandro Rinaldo |
| 2024 | Sim2Real-Fire: A Multi-modal Simulation Dataset for Forecast and Backtracking of Real-world Forest Fire. Yanzhi Li, Keqiu Li, Li Guohui, Zumin Wang, Changqing Ji, Lubo Wang, Die Zuo, Qing Guo, Feng Zhang, Manyu Wang, Di Lin |
| 2024 | SimGen: Simulator-conditioned Driving Scene Generation. Yunsong Zhou, Michael Simon, Zhenghao Mark Peng, Sicheng Mo, Hongzi Zhu, Minyi Guo, Bolei Zhou |
| 2024 | SimPO: Simple Preference Optimization with a Reference-Free Reward. Yu Meng, Mengzhou Xia, Danqi Chen |
| 2024 | SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion. Ming Dai, Lingfeng Yang, Yihao Xu, Zhenhua Feng, Wankou Yang |
| 2024 | Similarity-Navigated Conformal Prediction for Graph Neural Networks. Jianqing Song, Jianguo Huang, Wenyu Jiang, Baoming Zhang, Shuangjie Li, Chongjun Wang |
| 2024 | Simple and Effective Masked Diffusion Language Models. Subham S. Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T. Chiu, Alexander Rush, Volodymyr Kuleshov |
| 2024 | Simple and Fast Distillation of Diffusion Models. Zhenyu Zhou, Defang Chen, Can Wang, Chun Chen, Siwei Lyu |
| 2024 | Simplified and Generalized Masked Diffusion for Discrete Data. Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, Michalis K. Titsias |
| 2024 | Simplifying Constraint Inference with Inverse Reinforcement Learning. Adriana Hugessen, Harley Wiltzer, Glen Berseth |
| 2024 | Simplifying Latent Dynamics with Softly State-Invariant World Models. Tankred Saanum, Peter Dayan, Eric Schulz |
| 2024 | Simulation-Free Training of Neural ODEs on Paired Data. Semin Kim, Jaehoon Yoo, Jinwoo Kim, Yeonwoo Cha, Saehoon Kim, Seunghoon Hong |
| 2024 | Single Image Reflection Separation via Dual-Stream Interactive Transformers. Qiming Hu, Hainuo Wang, Xiaojie Guo |
| 2024 | Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models. Jiaqi Li, Qianshan Wei, Chuanyi Zhang, Guilin Qi, Miaozeng Du, Yongrui Chen, Sheng Bi, Fan Liu |
| 2024 | Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions. Quanqi Hu, Qi Qi, Zhaosong Lu, Tianbao Yang |
| 2024 | Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information. Marco Miani, Lorenzo Beretta, Søren Hauberg |
| 2024 | Sketching for Distributed Deep Learning: A Sharper Analysis. Mayank Shrivastava, Berivan Isik, Qiaobo Li, Sanmi Koyejo, Arindam Banerjee |
| 2024 | Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning. Yijun Dong, Viet Hoang Phan, Xiang Pan, Qi Lei |
| 2024 | SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions. Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, Peter Stone |
| 2024 | Skill-aware Mutual Information Optimisation for Zero-shot Generalisation in Reinforcement Learning. Xuehui Yu, Mhairi Dunion, Xin Li, Stefano V. Albrecht |
| 2024 | Skinned Motion Retargeting with Dense Geometric Interaction Perception. Zijie Ye, Jia-Wei Liu, Jia Jia, Shikun Sun, Mike Zheng Shou |
| 2024 | SkipPredict: When to Invest in Predictions for Scheduling. Rana Shahout, Michael Mitzenmacher |
| 2024 | Slack-Free Spiking Neural Network Formulation for Hypergraph Minimum Vertex Cover. Tam Nguyen, Anh-Dzung Doan, Zhipeng Cai, Tat-Jun Chin |
| 2024 | SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents. Ethan Rathbun, Christopher Amato, Alina Oprea |
| 2024 | Slice-100K: A Multimodal Dataset for Extrusion-based 3D Printing. Anushrut Jignasu, Kelly O. Marshall, Ankush Kumar Mishra, Lucas Nerone Rillo, Baskar Ganapathysubramanian, Aditya Balu, Chinmay Hegde, Adarsh Krishnamurthy |
| 2024 | Slicing Vision Transformer for Flexible Inference. Yitian Zhang, Huseyin Coskun, Xu Ma, Huan Wang, Ke Ma, Xi Stephen Chen, Derek Hao Hu, Yun Fu |
| 2024 | Slight Corruption in Pre-training Data Makes Better Diffusion Models. Hao Chen, Yujin Han, Diganta Misra, Xiang Li, Kai Hu, Difan Zou, Masashi Sugiyama, Jindong Wang, Bhiksha Raj |
| 2024 | SlimGPT: Layer-wise Structured Pruning for Large Language Models. Gui Ling, Ziyang Wang, Yuliang Yan, Qingwen Liu |
| 2024 | SlimSAM: 0.1% Data Makes Segment Anything Slim. Zigeng Chen, Gongfan Fang, Xinyin Ma, Xinchao Wang |
| 2024 | Slot State Space Models. Jindong Jiang, Fei Deng, Gautam Singh, Minseung Lee, Sungjin Ahn |
| 2024 | Slot-VLM: Object-Event Slots for Video-Language Modeling. Jiaqi Xu, Cuiling Lan, Wenxuan Xie, Xuejin Chen, Yan Lu |
| 2024 | SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM. Ming Nie, Dan Ding, Chunwei Wang, Yuanfan Guo, Jianhua Han, Hang Xu, Li Zhang |
| 2024 | Sm: enhanced localization in Multiple Instance Learning for medical imaging classification. Francisco M. Castro-Macías, Pablo Morales-Alvarez, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos |
| 2024 | Small coresets via negative dependence: DPPs, linear statistics, and concentration. Rémi Bardenet, Subhroshekhar Ghosh, Hugo Simon-Onfroy, Hoang Son Tran |
| 2024 | Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates. Jincheng Mei, Bo Dai, Alekh Agarwal, Sharan Vaswani, Anant Raj, Csaba Szepesvári, Dale Schuurmans |
| 2024 | SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models. Yu Yang, Siddhartha Mishra, Jeffrey N. Chiang, Baharan Mirzasoleiman |
| 2024 | Smoke and Mirrors in Causal Downstream Tasks. Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, Francesco Locatello |
| 2024 | Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention. Susung Hong |
| 2024 | Smoothed Online Classification can be Harder than Batch Classification. Vinod Raman, Unique Subedi, Ambuj Tewari |
| 2024 | Smoothie: Label Free Language Model Routing. Neel Guha, Mayee F. Chen, Trevor Chow, Ishan S. Khare, Christopher Ré |
| 2024 | SnapKV: LLM Knows What You are Looking for Before Generation. Yuhong Li, Yingbing Huang, Bowen Yang, Bharat Venkitesh, Acyr Locatelli, Hanchen Ye, Tianle Cai, Patrick Lewis, Deming Chen |
| 2024 | SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization. Wanhua Li, Zibin Meng, Jiawei Zhou, Donglai Wei, Chuang Gan, Hanspeter Pfister |
| 2024 | SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models. Jiayu Liu, Zhenya Huang, Tong Xiao, Jing Sha, Jinze Wu, Qi Liu, Shijin Wang, Enhong Chen |
| 2024 | Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann |
| 2024 | Soft Superpixel Neighborhood Attention. Kent W. Gauen, Stanley H. Chan |
| 2024 | Soft ascent-descent as a stable and flexible alternative to flooding. Matthew J. Holland, Kosuke Nakatani |
| 2024 | Soft-Label Integration for Robust Toxicity Classification. Zelei Cheng, Xian Wu, Jiahao Yu, Shuo Han, Xin-Qiang Cai, Xinyu Xing |
| 2024 | SolarCube: An Integrative Benchmark Dataset Harnessing Satellite and In-situ Observations for Large-scale Solar Energy Forecasting. Ruohan Li, Yiqun Xie, Xiaowei Jia, Dongdong Wang, Yanhua Li, Yingxue Zhang, Zhihao Wang, Zhili Li |
| 2024 | Solving Inverse Problems via Diffusion Optimal Control. Henry Li, Marcus Pereira |
| 2024 | Solving Minimum-Cost Reach Avoid using Reinforcement Learning. Oswin So, Cheng Ge, Chuchu Fan |
| 2024 | Solving Sparse \& High-Dimensional-Output Regression via Compression. Renyuan Li, Zhehui Chen, Guanyi Wang |
| 2024 | Solving Zero-Sum Markov Games with Continuous State via Spectral Dynamic Embedding. Chenhao Zhou, Zebang Shen, Zhang Chao, Hanbin Zhao, Hui Qian |
| 2024 | SongCreator: Lyrics-based Universal Song Generation. Shun Lei, Yixuan Zhou, Boshi Tang, Max W. Y. Lam, Feng Liu, Hangyu Liu, Jingcheng Wu, Shiyin Kang, Zhiyong Wu, Helen Meng |
| 2024 | Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases. Zian Su, Xiangzhe Xu, Ziyang Huang, Kaiyuan Zhang, Xiangyu Zhang |
| 2024 | Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation. Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H. Macke |
| 2024 | SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network. Weiyu Guo, Ying Sun, Yijie Xu, Ziyue Qiao, Yongkui Yang, Hui Xiong |
| 2024 | SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead. Minsu Kim, Walid Saad, Mérouane Debbah, Choong Seon Hong |
| 2024 | Space-Time Continuous PDE Forecasting using Equivariant Neural Fields. David M. Knigge, David R. Wessels, Riccardo Valperga, Samuele Papa, Jan-Jakob Sonke, Erik J. Bekkers, Efstratios Gavves |
| 2024 | SpaceByte: Towards Deleting Tokenization from Large Language Modeling. Kevin Slagle |
| 2024 | Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs. Matthew Zurek, Yudong Chen |
| 2024 | Sparse Bayesian Generative Modeling for Compressive Sensing. Benedikt Böck, Sadaf Syed, Wolfgang Utschick |
| 2024 | Sparse High Rank Adapters. Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi, Viswanath Ganapathy, Shreya Kadambi, Rafael Esteves, Shubhankar Borse, Paul N. Whatmough, Risheek Garrepalli, Mart van Baalen, Harris Teague, Markus Nagel |
| 2024 | Sparse maximal update parameterization: A holistic approach to sparse training dynamics. Nolan Dey, Shane Bergsma, Joel Hestness |
| 2024 | Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis. Qitao Zhao, Shubham Tulsiani |
| 2024 | SparseLLM: Towards Global Pruning of Pre-trained Language Models. Guangji Bai, Yijiang Li, Chen Ling, Kibaek Kim, Liang Zhao |
| 2024 | Sparsity-Agnostic Linear Bandits with Adaptive Adversaries. Tianyuan Jin, Kyoungseok Jang, Nicolò Cesa-Bianchi |
| 2024 | SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors. Chenyang Ma, Kai Lu, Ta Ying Cheng, Niki Trigoni, Andrew Markham |
| 2024 | SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models. An-Chieh Cheng, Hongxu Yin, Yang Fu, Qiushan Guo, Ruihan Yang, Jan Kautz, Xiaolong Wang, Sifei Liu |
| 2024 | Spatio-Spectral Graph Neural Networks. Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann |
| 2024 | Spatio-Temporal Interactive Learning for Efficient Image Reconstruction of Spiking Cameras. Bin Fan, Jiaoyang Yin, Yuchao Dai, Chao Xu, Tiejun Huang, Boxin Shi |
| 2024 | SpeAr: A Spectral Approach for Zero-Shot Node Classification. Ting Guo, Da Wang, Jiye Liang, Kaihan Zhang, Jianchao Zeng |
| 2024 | Speaking Your Language: Spatial Relationships in Interpretable Emergent Communication. Olaf Lipinski, Adam J. Sobey, Federico Cerutti, Timothy J. Norman |
| 2024 | Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting. Ziyi Yang, Xinyu Gao, Yang-Tian Sun, Yihua Huang, Xiaoyang Lyu, Wen Zhou, Shaohui Jiao, Xiaojuan Qi, Xiaogang Jin |
| 2024 | SpecExec: Massively Parallel Speculative Decoding For Interactive LLM Inference on Consumer Devices. Ruslan Svirschevski, Avner May, Zhuoming Chen, Beidi Chen, Zhihao Jia, Max Ryabinin |
| 2024 | Spectral Adapter: Fine-Tuning in Spectral Space. Fangzhao Zhang, Mert Pilanci |
| 2024 | Spectral Editing of Activations for Large Language Model Alignment. Yifu Qiu, Zheng Zhao, Yftah Ziser, Anna Korhonen, Edoardo Maria Ponti, Shay B. Cohen |
| 2024 | Spectral Graph Pruning Against Over-Squashing and Over-Smoothing. Adarsh Jamadandi, Celia Rubio-Madrigal, Rebekka Burkholz |
| 2024 | Spectral Learning of Shared Dynamics Between Generalized-Linear Processes. Lucine L. Oganesian, Omid G. Sani, Maryam Shanechi |
| 2024 | Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees. Dohyeong Kim, Taehyun Cho, Seungyub Han, Hojun Chung, Kyungjae Lee, Songhwai Oh |
| 2024 | Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration. Zhuofan Wen, Shangtong Gui, Yang Feng |
| 2024 | Speculative Monte-Carlo Tree Search. Scott Cheng, Mahmut T. Kandemir, Ding-Yong Hong |
| 2024 | SpeechAlign: Aligning Speech Generation to Human Preferences. Dong Zhang, Zhaowei Li, Shimin Li, Xin Zhang, Pengyu Wang, Yaqian Zhou, Xipeng Qiu |
| 2024 | SpeechForensics: Audio-Visual Speech Representation Learning for Face Forgery Detection. Yachao Liang, Min Yu, Gang Li, Jianguo Jiang, Boquan Li, Feng Yu, Ning Zhang, Xiang Meng, Weiqing Huang |
| 2024 | SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation. Yiqi Zhang, Yang You |
| 2024 | SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision. Kseniya Cherenkova, Elona Dupont, Anis Kacem, Gleb Gusev, Djamila Aouada |
| 2024 | Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation. Yu Zheng, Guangming Wang, Jiuming Liu, Marc Pollefeys, Hesheng Wang |
| 2024 | Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? Ruisheng Cao, Fangyu Lei, Haoyuan Wu, Jixuan Chen, Yeqiao Fu, Hongcheng Gao, Xinzhuang Xiong, Hanchong Zhang, Wenjing Hu, Yuchen Mao, Tianbao Xie, Hongshen Xu, Danyang Zhang, Sida I. Wang, Ruoxi Sun, Pengcheng Yin, Caiming Xiong, Ansong Ni, Qian Liu, Victor Zhong, Lu Chen, Kai Yu, Tao Yu |
| 2024 | Spike-based Neuromorphic Model for Sound Source Localization. Dehao Zhang, Shuai Wang, Ammar Belatreche, Wenjie Wei, Yichen Xiao, Haorui Zheng, Zijian Zhou, Malu Zhang, Yang Yang |
| 2024 | SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams. Kang Chen, Shiyan Chen, Jiyuan Zhang, Baoyue Zhang, Yajing Zheng, Tiejun Huang, Zhaofei Yu |
| 2024 | SpikedAttention: Training-Free and Fully Spike-Driven Transformer-to-SNN Conversion with Winner-Oriented Spike Shift for Softmax Operation. Sangwoo Hwang, Seunghyun Lee, Dahoon Park, Donghun Lee, Jaeha Kung |
| 2024 | Spiking Graph Neural Network on Riemannian Manifolds. Li Sun, Zhenhao Huang, Qiqi Wan, Hao Peng, Philip S. Yu |
| 2024 | Spiking Neural Network as Adaptive Event Stream Slicer. Jiahang Cao, Mingyuan Sun, Ziqing Wang, Hao Cheng, Qiang Zhang, Shibo Zhou, Renjing Xu |
| 2024 | Spiking Token Mixer: An event-driven friendly Former structure for spiking neural networks. Shikuang Deng, Yuhang Wu, Kangrui Du, Shi Gu |
| 2024 | Spiking Transformer with Experts Mixture. Zhaokun Zhou, Yijie Lu, Yanhao Jia, Kaiwei Che, Jun Niu, Liwei Huang, Xinyu Shi, Yuesheng Zhu, Guoqi Li, Zhaofei Yu, Li Yuan |
| 2024 | Splatter a Video: Video Gaussian Representation for Versatile Processing. Yang-Tian Sun, Yihua Huang, Lin Ma, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan Qi |
| 2024 | SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation. Jesus Zarzar, Bernard Ghanem |
| 2024 | SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation. Zeyao Ma, Bohan Zhang, Jing Zhang, Jifan Yu, Xiaokang Zhang, Xiaohan Zhang, Sijia Luo, Xi Wang, Jie Tang |
| 2024 | Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics. Weitong Zhang, Chengqi Zang, Liu Li, Sarah Cechnicka, Cheng Ouyang, Bernhard Kainz |
| 2024 | Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation. Kaibo Zhang, Yunjuan Wang, Raman Arora |
| 2024 | Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness. Xiaoge Deng, Tao Sun, Shengwei Li, Dongsheng Li, Xicheng Lu |
| 2024 | Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective. Yongxin Zhu, Bocheng Li, Hang Zhang, Xin Li, Linli Xu, Lidong Bing |
| 2024 | Stabilized Proximal-Point Methods for Federated Optimization. Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich |
| 2024 | Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling. Skyler Wu, Fred Lu, Edward Raff, James Holt |
| 2024 | Stabilizing Zero-Shot Prediction: A Novel Antidote to Forgetting in Continual Vision-Language Tasks. Zijian Gao, Xingxing Zhang, Kele Xu, Xinjun Mao, Huaimin Wang |
| 2024 | Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes. Dan Qiao, Kaiqi Zhang, Esha Singh, Daniel Soudry, Yu-Xiang Wang |
| 2024 | Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation. Jiajun Wang, Morteza Ghahremani, Yitong Li, Björn Ommer, Christian Wachinger |
| 2024 | StackEval: Benchmarking LLMs in Coding Assistance. Nidhish Shah, Zulkuf Genc, Dogu Araci |
| 2024 | Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training. Wenyu Du, Tongxu Luo, Zihan Qiu, Zeyu Huang, Yikang Shen, Reynold Cheng, Yike Guo, Jie Fu |
| 2024 | Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning. Hang Zhou, Yehui Tang, Haochen Qin, Yujie Yang, Renren Jin, Deyi Xiong, Kai Han, Yunhe Wang |
| 2024 | State Chrono Representation for Enhancing Generalization in Reinforcement Learning. Jianda Chen, Wen Zheng Terence Ng, Zichen Chen, Sinno Jialin Pan, Tianwei Zhang |
| 2024 | State Space Models on Temporal Graphs: A First-Principles Study. Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng |
| 2024 | State-free Reinforcement Learning. Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang |
| 2024 | Statistical Efficiency of Distributional Temporal Difference Learning. Yang Peng, Liangyu Zhang, Zhihua Zhang |
| 2024 | Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm. Leo Zhou, Joao Basso, Song Mei |
| 2024 | Statistical Multicriteria Benchmarking via the GSD-Front. Christoph Jansen, Georg Schollmeyer, Julian Rodemann, Hannah Blocher, Thomas Augustin |
| 2024 | Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence. Anna Korba, Francis R. Bach, Clémentine Chazal |
| 2024 | Statistical-Computational Trade-offs for Density Estimation. Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal, Haike Xu |
| 2024 | Stealth edits to large language models. Oliver J. Sutton, Qinghua Zhou, Wei Wang, Desmond J. Higham, Alexander N. Gorban, Alexander Bastounis, Ivan Tyukin |
| 2024 | StepbaQ: Stepping backward as Correction for Quantized Diffusion Models. Yi-Chung Chen, Zhi-Kai Huang, Jing-Ren Chen |
| 2024 | Stepping Forward on the Last Mile. Chen Feng, Jay Zhuo, Parker Zhang, Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Andrew Zou Li |
| 2024 | Stepping on the Edge: Curvature Aware Learning Rate Tuners. Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa |
| 2024 | Stepwise Alignment for Constrained Language Model Policy Optimization. Akifumi Wachi, Thien Q. Tran, Rei Sato, Takumi Tanabe, Youhei Akimoto |
| 2024 | Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution. Ian Covert, Chanwoo Kim, Su-In Lee, James Y. Zou, Tatsunori B. Hashimoto |
| 2024 | Stochastic Concept Bottleneck Models. Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt |
| 2024 | Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements. Jiseok Chae, Chulhee Yun, Donghwan Kim |
| 2024 | Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines. Edward Milsom, Ben Anson, Laurence Aitchison |
| 2024 | Stochastic Newton Proximal Extragradient Method. Ruichen Jiang, Michal Derezinski, Aryan Mokhtari |
| 2024 | Stochastic Optimal Control Matching. Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen |
| 2024 | Stochastic Optimal Control and Estimation with Multiplicative and Internal Noise. Francesco Damiani, Akiyuki Anzai, Jan Drugowitsch, Gregory C. DeAngelis, Rubén Moreno-Bote |
| 2024 | Stochastic Optimal Control for Diffusion Bridges in Function Spaces. Byoungwoo Park, Jungwon Choi, Sungbin Lim, Juho Lee |
| 2024 | Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data. Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian |
| 2024 | Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss. Qiang Li, Hoi-To Wai |
| 2024 | Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators. Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi |
| 2024 | Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity. Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee |
| 2024 | Stochastic contextual bandits with graph feedback: from independence number to MAS number. Yuxiao Wen, Yanjun Han, Zhengyuan Zhou |
| 2024 | Stopping Bayesian Optimization with Probabilistic Regret Bounds. James T. Wilson |
| 2024 | StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation. Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng, Jiashi Feng, Qibin Hou |
| 2024 | Strategic Linear Contextual Bandits. Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, Haifeng Xu |
| 2024 | Strategic Littlestone Dimension: Improved Bounds on Online Strategic Classification. Saba Ahmadi, Kunhe Yang, Hanrui Zhang |
| 2024 | Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting. Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet |
| 2024 | StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving. Chang Gao, Haiyun Jiang, Deng Cai, Shuming Shi, Wai Lam |
| 2024 | Stratified Prediction-Powered Inference for Effective Hybrid Evaluation of Language Models. Adam Fisch, Joshua Maynez, R. Alex Hofer, Bhuwan Dhingra, Amir Globerson, William W. Cohen |
| 2024 | StreamBench: Towards Benchmarking Continuous Improvement of Language Agents. Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, Hung-yi Lee |
| 2024 | StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences. Shangkun Sun, Jiaming Liu, Huaxia Li, Guoqing Liu, Thomas H. Li, Wei Gao |
| 2024 | Streaming Bayes GFlowNets. Tiago da Silva, Daniel Augusto de Souza, Diego Mesquita |
| 2024 | Streaming Detection of Queried Event Start. Cristóbal Eyzaguirre, Eric Tang, Shyamal Buch, Adrien Gaidon, Jiajun Wu, Juan Carlos Niebles |
| 2024 | Streaming Long Video Understanding with Large Language Models. Rui Qian, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Shuangrui Ding, Dahua Lin, Jiaqi Wang |
| 2024 | StreamingDialogue: Prolonged Dialogue Learning via Long Context Compression with Minimal Losses. Jianan Li, Quan Tu, Cunli Mao, Zhengtao Yu, Ji-Rong Wen, Rui Yan |
| 2024 | Stress-Testing Capability Elicitation With Password-Locked Models. Ryan Greenblatt, Fabien Roger, Dmitrii Krasheninnikov, David Krueger |
| 2024 | Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack. Xiaoyue Xu, Qinyuan Ye, Xiang Ren |
| 2024 | Stronger Than You Think: Benchmarking Weak Supervision on Realistic Tasks. Tianyi Zhang, Linrong Cai, Jeffrey Li, Nicholas Roberts, Neel Guha, Frederic Sala |
| 2024 | Structural Inference of Dynamical Systems with Conjoined State Space Models. Aoran Wang, Jun Pang |
| 2024 | Structure Consistent Gaussian Splatting with Matching Prior for Few-shot Novel View Synthesis. Rui Peng, Wangze Xu, Luyang Tang, Levio Leo, Jianbo Jiao, Ronggang Wang |
| 2024 | Structured Learning of Compositional Sequential Interventions. Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva |
| 2024 | Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics. Xiaodan Chen, Xiucheng Li, Xinyang Chen, Zhijun Li |
| 2024 | Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling. Jingwei Zhao, Gus Xia, Ziyu Wang, Ye Wang |
| 2024 | Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning. Arijit Sehanobish, Kumar Avinava Dubey, Krzysztof Marcin Choromanski, Somnath Basu Roy Chowdhury, Deepali Jain, Vikas Sindhwani, Snigdha Chaturvedi |
| 2024 | Structured flexibility in recurrent neural networks via neuromodulation. Julia Costacurta, Shaunak Bhandarkar, David M. Zoltowski, Scott W. Linderman |
| 2024 | Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation. Yuwu Lu, Haoyu Huang, Xue Hu |
| 2024 | Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models. Matthew Zheng, Enis Simsar, Hidir Yesiltepe, Federico Tombari, Joel Simon, Pinar Yanardag Delul |
| 2024 | Stylus: Automatic Adapter Selection for Diffusion Models. Michael Luo, Justin Wong, Brandon Trabucco, Yanping Huang, Joseph E. Gonzalez, Zhifeng Chen, Ruslan Salakhutdinov, Ion Stoica |
| 2024 | Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning. Riccardo Poiani, Gabriele Curti, Alberto Maria Metelli, Marcello Restelli |
| 2024 | SubgDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning. Jiying Zhang, Zijing Liu, Yu Wang, Bin Feng, Yu Li |
| 2024 | SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis. Huzaifa Pardawala, Siddhant Sukhani, Agam Shah, Veer Kejriwal, Abhishek Pillai, Rohan Bhasin, Andrew DiBiasio, Tarun Mandapati, Dhruv Adha, Sudheer Chava |
| 2024 | Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning. Yanting Miao, William Loh, Suraj Kothawade, Pascal Poupart, Abdullah Rashwan, Yeqing Li |
| 2024 | Subsurface Scattering for Gaussian Splatting. Jan-Niklas Dihlmann, Arjun Majumdar, Andreas Engelhardt, Raphael Braun, Hendrik P. A. Lensch |
| 2024 | Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning. David Yunis, Justin Jung, Falcon Z. Dai, Matthew R. Walter |
| 2024 | Suitable is the Best: Task-Oriented Knowledge Fusion in Vulnerability Detection. Jingjing Wang, Minhuan Huang, Yuanping Nie, Xiang Li, Qianjin Du, Wei Kong, Huan Deng, Xiaohui Kuang |
| 2024 | Super Consistency of Neural Network Landscapes and Learning Rate Transfer. Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto |
| 2024 | SuperDeepFool: a new fast and accurate minimal adversarial attack. Alireza Abdollahpour, Mahed Abroshan, Seyed-Mohsen Moosavi-Dezfooli |
| 2024 | SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition. Feng Lu, Xinyao Zhang, Canming Ye, Shuting Dong, Lijun Zhang, Xiangyuan Lan, Chun Yuan |
| 2024 | Superposed Decoding: Multiple Generations from a Single Autoregressive Inference Pass. Ethan Shen, Alan Fan, Sarah M. Pratt, Jae Sung Park, Matthew Wallingford, Sham M. Kakade, Ari Holtzman, Ranjay Krishna, Ali Farhadi, Aditya Kusupati |
| 2024 | Supervised Kernel Thinning. Albert Gong, Kyuseong Choi, Raaz Dwivedi |
| 2024 | Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques. Benyuan Meng, Qianqian Xu, Zitai Wang, Zhiyong Yang, Xiaochun Cao, Qingming Huang |
| 2024 | Supra-Laplacian Encoding for Transformer on Dynamic Graphs. Yannis Karmim, Marc Lafon, Raphaël Fournier-S'niehotta, Nicolas Thome |
| 2024 | SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation. Misha Khodak, Lester Mackey, Alexandra Chouldechova, Miro Dudík |
| 2024 | Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling. Shuaipeng Li, Penghao Zhao, Hailin Zhang, Xingwu Sun, Hao Wu, Dian Jiao, Weiyan Wang, Chengjun Liu, Zheng Fang, Jinbao Xue, Yangyu Tao, Bin Cui, Di Wang |
| 2024 | SurgicAI: A Hierarchical Platform for Fine-Grained Surgical Policy Learning and Benchmarking. Jin Wu, Haoying Zhou, Peter Kazanzides, Adnan Munawar, Anqi Liu |
| 2024 | SustainDC: Benchmarking for Sustainable Data Center Control. Avisek Naug, Antonio Guillen, Ricardo Luna Gutierrez, Vineet Gundecha, Cullen E. Bash, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Dejan Markovikj, Lekhapriya Dheeraj Kashyap, Desik Rengarajan, Soumyendu Sarkar |
| 2024 | Swift Sampler: Efficient Learning of Sampler by 10 Parameters. Jiawei Yao, Chuming Li, Canran Xiao |
| 2024 | SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention. Róbert Csordás, Piotr Piekos, Kazuki Irie, Jürgen Schmidhuber |
| 2024 | SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization. Qian Chen, Tianjian Zhang, Linxin Yang, Qingyu Han, Akang Wang, Ruoyu Sun, Xiaodong Luo, Tsung-Hui Chang |
| 2024 | Symbolic Regression with a Learned Concept Library. Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles D. Cranmer, Swarat Chaudhuri |
| 2024 | Symmetric Linear Bandits with Hidden Symmetry. Nam Phuong Tran, The-Anh Ta, Debmalya Mandal, Long Tran-Thanh |
| 2024 | Symmetries in Overparametrized Neural Networks: A Mean Field View. Javier Maass Martínez, Joaquín Fontbona |
| 2024 | Symmetry Discovery Beyond Affine Transformations. Ben Shaw, Abram Magner, Kevin R. Moon |
| 2024 | Symmetry-Informed Governing Equation Discovery. Jianke Yang, Wang Rao, Nima Dehmamy, Robin Walters, Rose Yu |
| 2024 | SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery. Jian Song, Hongruixuan Chen, Weihao Xuan, Junshi Xia, Naoto Yokoya |
| 2024 | Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale. Tianyue Ou, Frank F. Xu, Aman Madaan, Jiarui Liu, Robert Lo, Abishek Sridhar, Sudipta Sengupta, Dan Roth, Graham Neubig, Shuyan Zhou |
| 2024 | SyncTweedies: A General Generative Framework Based on Synchronized Diffusions. Jaihoon Kim, Juil Koo, Kyeongmin Yeo, Minhyuk Sung |
| 2024 | SyncVIS: Synchronized Video Instance Segmentation. Rongkun Zheng, Lu Qi, Xi Chen, Yi Wang, Kun Wang, Yu Qiao, Hengshuang Zhao |
| 2024 | Synergistic Dual Spatial-aware Generation of Image-to-text and Text-to-image. Yu Zhao, Hao Fei, Xiangtai Li, Libo Qin, Jiayi Ji, Hongyuan Zhu, Meishan Zhang, Min Zhang, Jianguo Wei |
| 2024 | Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models. Yeming Wen, Swarat Chaudhuri |
| 2024 | Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages. Federico Mora, Justin Wong, Haley Lepe, Sahil Bhatia, Karim Elmaaroufi, George Varghese, Joseph E. Gonzalez, Elizabeth Polgreen, Sanjit A. Seshia |
| 2024 | T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback. Jiachen Li, Weixi Feng, Tsu-Jui Fu, Xinyi Wang, Sugato Basu, Wenhu Chen, William Yang Wang |
| 2024 | T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models. Yibo Miao, Yifan Zhu, Lijia Yu, Jun Zhu, Xiao-Shan Gao, Yinpeng Dong |
| 2024 | T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness Recognition. Chen Yeh, You-Ming Chang, Wei-Chen Chiu, Ning Yu |
| 2024 | TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools. Avi Caciularu, Alon Jacovi, Eyal Ben-David, Sasha Goldshtein, Tal Schuster, Jonathan Herzig, Gal Elidan, Amir Globerson |
| 2024 | TAIA: Large Language Models are Out-of-Distribution Data Learners. Shuyang Jiang, Yusheng Liao, Ya Zhang, Yanfeng Wang, Yu Wang |
| 2024 | TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight. Hyun-Kurl Jang, Jihun Kim, Hyeokjun Kweon, Kuk-Jin Yoon |
| 2024 | TAPTRv2: Attention-based Position Update Improves Tracking Any Point. Hongyang Li, Hao Zhang, Shilong Liu, Zhaoyang Zeng, Feng Li, Bohan Li, Tianhe Ren, Lei Zhang |
| 2024 | TAPVid-3D: A Benchmark for Tracking Any Point in 3D. Skanda Koppula, Ignacio Rocco, Yi Yang, Joseph Heyward, João Carreira, Andrew Zisserman, Gabriel Brostow, Carl Doersch |
| 2024 | TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models. Zhen Chen, Yi Zhang, Fu Wang, Xingyu Zhao, Xiaowei Huang, Wenjie Ruan |
| 2024 | TARSS-Net: Temporal-Aware Radar Semantic Segmentation Network. Youcheng Zhang, Liwen Zhang, ZijunHu, Pengcheng Pi, Teng Li, Yuanpei Chen, Shi Peng, Zhe Ma |
| 2024 | TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs. Zhuofeng Li, Zixing Gou, Xiangnan Zhang, Zhongyuan Liu, Sirui Li, Yuntong Hu, Chen Ling, Zheng Zhang, Liang Zhao |
| 2024 | TFG: Unified Training-Free Guidance for Diffusion Models. Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Y. Zou, Stefano Ermon |
| 2024 | TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering. Jun Dan, Weiming Liu, Chunfeng Xie, Hua Yu, Shunjie Dong, Yanchao Tan |
| 2024 | TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene. Sandika Biswas, Qianyi Wu, Biplab Banerjee, Hamid Rezatofighi |
| 2024 | TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs. Julia Gastinger, Shenyang Huang, Michael Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Ioannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, Guillaume Rabusseau |
| 2024 | TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment. Wei Li, Hehe Fan, Yongkang Wong, Mohan S. Kankanhalli, Yi Yang |
| 2024 | TPC: Test-time Procrustes Calibration for Diffusion-based Human Image Animation. Sunjae Yoon, Gwanhyeong Koo, Younghwan Lee, Chang Dong Yoo |
| 2024 | TPR: Topology-Preserving Reservoirs for Generalized Zero-Shot Learning. Hui Chen, Yanbin Liu, Yongqiang Ma, Nanning Zheng, Xin Yu |
| 2024 | TSDS: Data Selection for Task-Specific Model Finetuning. Zifan Liu, Amin Karbasi, Theodoros Rekatsinas |
| 2024 | TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series. Alexander Nikitin, Letizia Iannucci, Samuel Kaski |
| 2024 | TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models. Andrei Margeloiu, Xiangjian Jiang, Nikola Simidjievski, Mateja Jamnik |
| 2024 | TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy. Weichao Zhao, Hao Feng, Qi Liu, Jingqun Tang, Binghong Wu, Lei Liao, Shu Wei, Yongjie Ye, Hao Liu, Wengang Zhou, Houqiang Li, Can Huang |
| 2024 | TableRAG: Million-Token Table Understanding with Language Models. Si-An Chen, Lesly Miculicich, Julian Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen, Yasuhisa Fujii, Hsuan-Tien Lin, Chen-Yu Lee, Tomas Pfister |
| 2024 | TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases. Thibault Simonetto, Salah Ghamizi, Maxime Cordy |
| 2024 | Tackling Uncertain Correspondences for Multi-Modal Entity Alignment. Liyi Chen, Ying Sun, Shengzhe Zhang, Yuyang Ye, Wei Wu, Hui Xiong |
| 2024 | Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation. Ruihan Gao, Kangle Deng, Gengshan Yang, Wenzhen Yuan, Jun-Yan Zhu |
| 2024 | Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks. Yufei Guo, Yuanpei Chen, Zecheng Hao, Weihang Peng, Zhou Jie, Yuhan Zhang, Xiaode Liu, Zhe Ma |
| 2024 | Talking Heads: Understanding Inter-Layer Communication in Transformer Language Models. Jack Merullo, Carsten Eickhoff, Ellie Pavlick |
| 2024 | Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces. Yaqi Duan, Martin J. Wainwright |
| 2024 | Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains. Lei Wang, Jieming Bian, Letian Zhang, Chen Chen, Jie Xu |
| 2024 | Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs. Qinpeng Cui, Yixuan Liu, Xinyi Zhang, Qiqi Bao, Qingmin Liao, liwang Amd, Lu Tian, Zicheng Liu, Zhongdao Wang, Emad Barsoum |
| 2024 | Taming Generative Diffusion Prior for Universal Blind Image Restoration. Siwei Tu, Weidong Yang, Ben Fei |
| 2024 | Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting. Duo Cheng, Xingyu Zhou, Bo Ji |
| 2024 | Taming the Long Tail in Human Mobility Prediction. Xiaohang Xu, Renhe Jiang, Chuang Yang, Zipei Fan, Kaoru Sezaki |
| 2024 | Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems. Kurt Butler, Daniel Waxman, Petar M. Djuric |
| 2024 | Target-Guided Adversarial Point Cloud Transformer Towards Recognition Against Real-world Corruptions. Jie Wang, Tingfa Xu, Lihe Ding, Jianan Li |
| 2024 | Targeted Sequential Indirect Experiment Design. Elisabeth Ailer, Niclas Dern, Jason S. Hartford, Niki Kilbertus |
| 2024 | Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings. Milad Khademi Nori, Il-Min Kim |
| 2024 | Task Me Anything. Jieyu Zhang, Weikai Huang, Zixian Ma, Oscar Michel, Dong He, Tanmay Gupta, Wei-Chiu Ma, Ali Farhadi, Aniruddha Kembhavi, Ranjay Krishna |
| 2024 | Task-Agnostic Machine-Learning-Assisted Inference. Jiacheng Miao, Qiongshi Lu |
| 2024 | Task-oriented Time Series Imputation Evaluation via Generalized Representers. Zhixian Wang, Linxiao Yang, Liang Sun, Qingsong Wen, Yi Wang |
| 2024 | Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning. Grzegorz Rypesc, Sebastian Cygert, Tomasz Trzcinski, Bartlomiej Twardowski |
| 2024 | TaskBench: Benchmarking Large Language Models for Task Automation. Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, Yueting Zhuang |
| 2024 | Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arik |
| 2024 | Team-Fictitious Play for Reaching Team-Nash Equilibrium in Multi-team Games. Ahmed Said Donmez, Yuksel Arslantas, Muhammed Omer Sayin |
| 2024 | Tell What You Hear From What You See - Video to Audio Generation Through Text. Xiulong Liu, Kun Su, Eli Shlizerman |
| 2024 | Template-free Articulated Gaussian Splatting for Real-time Reposable Dynamic View Synthesis. Diwen Wan, Yuxiang Wang, Ruijie Lu, Gang Zeng |
| 2024 | Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed. Katherine Tieu, Dongqi Fu, Yada Zhu, Hendrik F. Hamann, Jingrui He |
| 2024 | Temporal Sentence Grounding with Relevance Feedback in Videos. Jianfeng Dong, Xiaoman Peng, Daizong Liu, Xiaoye Qu, Xun Yang, Cuizhu Bao, Meng Wang |
| 2024 | Temporal-Difference Learning Using Distributed Error Signals. Jonas Guan, Shon Eduard Verch, Claas Voelcker, Ethan C. Jackson, Nicolas Papernot, William A. Cunningham |
| 2024 | Temporally Consistent Atmospheric Turbulence Mitigation with Neural Representations. Haoming Cai, Jingxi Chen, Brandon Y. Feng, Weiyun Jiang, Mingyang Xie, Kevin Zhang, Cornelia Fermüller, Yiannis Aloimonos, Ashok Veeraraghavan, Christopher A. Metzler |
| 2024 | Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor. Daniel Miao, Gilad Lerman, Joe Kileel |
| 2024 | Terra: A Multimodal Spatio-Temporal Dataset Spanning the Earth. Wei Chen, Xixuan Hao, Yuankai Wu, Yuxuan Liang |
| 2024 | Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning. Stefan Pranger, Hana Chockler, Martin Tappler, Bettina Könighofer |
| 2024 | Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line. Eungyeup Kim, Mingjie Sun, Christina Baek, Aditi Raghunathan, J. Zico Kolter |
| 2024 | Test-Time Dynamic Image Fusion. Bing Cao, Yinan Xia, Yi Ding, Changqing Zhang, Qinghua Hu |
| 2024 | Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment. Zhen-Yu Zhang, Zhiyu Xie, Huaxiu Yao, Masashi Sugiyama |
| 2024 | Testably Learning Polynomial Threshold Functions. Lucas Slot, Stefan Tiegel, Manuel Wiedmer |
| 2024 | Testing Calibration in Nearly-Linear Time. Lunjia Hu, Arun Jambulapati, Kevin Tian, Chutong Yang |
| 2024 | Testing Semantic Importance via Betting. Jacopo Teneggi, Jeremias Sulam |
| 2024 | Tetrahedron Splatting for 3D Generation. Chun Gu, Zeyu Yang, Zijie Pan, Xiatian Zhu, Li Zhang |
| 2024 | Text to Blind Motion. Hee Jae Kim, Kathakoli Sengupta, Masaki Kuribayashi, Hernisa Kacorri, Eshed Ohn-Bar |
| 2024 | Text-Aware Diffusion for Policy Learning. Calvin Luo, Mandy He, Zilai Zeng, Chen Sun |
| 2024 | Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model. Hao Zhang, Lei Cao, Jiayi Ma |
| 2024 | Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models. Lu Yu, Haiyang Zhang, Changsheng Xu |
| 2024 | Text-Infused Attention and Foreground-Aware Modeling for Zero-Shot Temporal Action Detection. Yearang Lee, Ho-Joong Kim, Seong-Whan Lee |
| 2024 | Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights. Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang |
| 2024 | Text2CAD: Generating Sequential CAD Designs from Beginner-to-Expert Level Text Prompts. Mohammad Sadil Khan, Sankalp Sinha, Talha Uddin Sheikh, Didier Stricker, Sk Aziz Ali, Muhammad Zeshan Afzal |
| 2024 | Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction. Haoran Luo, Haihong E, Yuhao Yang, Tianyu Yao, Yikai Guo, Zichen Tang, Wentai Zhang, Shiyao Peng, Kaiyang Wan, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu |
| 2024 | TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control. Weichao Zeng, Yan Shu, Zhenhang Li, Dongbao Yang, Yu Zhou |
| 2024 | Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection. Saehyung Lee, Jisoo Mok, Sangha Park, Yongho Shin, Dahuin Jung, Sungroh Yoon |
| 2024 | The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators. Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala |
| 2024 | The Art of Saying No: Contextual Noncompliance in Language Models. Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, Pradeep Dasigi, Valentina Pyatkin, Abhilasha Ravichander, Sarah Wiegreffe, Nouha Dziri, Khyathi Raghavi Chandu, Jack Hessel, Yulia Tsvetkov, Noah A. Smith, Yejin Choi, Hanna Hajishirzi |
| 2024 | The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons. Eryn Sale, Wenhao Zhang |
| 2024 | The Benefits of Balance: From Information Projections to Variance Reduction. Lang Liu, Ronak Mehta, Soumik Pal, Zaïd Harchaoui |
| 2024 | The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection. Qingyang Zhang, Qiuxuan Feng, Joey Tianyi Zhou, Yatao Bian, Qinghua Hu, Changqing Zhang |
| 2024 | The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks. Andrea Bonfanti, Giuseppe Bruno, Cristina Cipriani |
| 2024 | The Closeness of In-Context Learning and Weight Shifting for Softmax Regression. Shuai Li, Zhao Song, Yu Xia, Tong Yu, Tianyi Zhou |
| 2024 | The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize. Dongyan Lucy Huo, Yixuan Zhang, Yudong Chen, Qiaomin Xie |
| 2024 | The Dormant Neuron Phenomenon in Multi-Agent Reinforcement Learning Value Factorization. Haoyuan Qin, Chennan Ma, Mian Deng, Zhengzhu Liu, Songzhu Mei, Xinwang Liu, Cheng Wang, Siqi Shen |
| 2024 | The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning. Anya Sims, Cong Lu, Jakob N. Foerster, Yee Whye Teh |
| 2024 | The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark. Qinghua Liu, John Paparrizos |
| 2024 | The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof. Derek Lim, Theo Putterman, Robin Walters, Haggai Maron, Stefanie Jegelka |
| 2024 | The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains. Ezra Edelman, Nikolaos Tsilivis, Benjamin L. Edelman, Eran Malach, Surbhi Goel |
| 2024 | The Expressive Capacity of State Space Models: A Formal Language Perspective. Yash Raj Sarrof, Yana Veitsman, Michael Hahn |
| 2024 | The Factorization Curse: Which Tokens You Predict Underlie the Reversal Curse and More. Ouail Kitouni, Niklas Nolte, Adina Williams, Michael Rabbat, Diane Bouchacourt, Mark Ibrahim |
| 2024 | The Fairness-Quality Tradeoff in Clustering. Rashida Hakim, Ana-Andreea Stoica, Christos H. Papadimitriou, Mihalis Yannakakis |
| 2024 | The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks. Lénaïc Chizat, Praneeth Netrapalli |
| 2024 | The Fine-Grained Complexity of Gradient Computation for Training Large Language Models. Josh Alman, Zhao Song |
| 2024 | The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale. Guilherme Penedo, Hynek Kydlícek, Loubna Ben Allal, Anton Lozhkov, Margaret Mitchell, Colin A. Raffel, Leandro von Werra, Thomas Wolf |
| 2024 | The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning. Jake Fawkes, Nic Fishman, Mel Andrews, Zachary C. Lipton |
| 2024 | The GAN is dead; long live the GAN! A Modern GAN Baseline. Nick Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin |
| 2024 | The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations. Tyler LaBonte, John C. Hill, Xinchen Zhang, Vidya Muthukumar, Abhishek Kumar |
| 2024 | The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms. Elizabeth Collins-Woodfin, Inbar Seroussi, Begoña García Malaxechebarría, Andrew W. Mackenzie, Elliot Paquette, Courtney Paquette |
| 2024 | The Impact of Geometric Complexity on Neural Collapse in Transfer Learning. Michael Munn, Benoit Dherin, Javier Gonzalvo |
| 2024 | The Impact of Initialization on LoRA Finetuning Dynamics. Soufiane Hayou, Nikhil Ghosh, Bin Yu |
| 2024 | The Implicit Bias of Adam on Separable Data. Chenyang Zhang, Difan Zou, Yuan Cao |
| 2024 | The Implicit Bias of Gradient Descent on Separable Multiclass Data. Hrithik Ravi, Clayton Scott, Daniel Soudry, Yutong Wang |
| 2024 | The Implicit Bias of Gradient Descent toward Collaboration between Layers: A Dynamic Analysis of Multilayer Perceptions. Zheng Wang, Geyong Min, Wenjie Ruan |
| 2024 | The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing. Yang Xu, Yihong Gu, Cong Fang |
| 2024 | The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains. Eric Qu, Aditi S. Krishnapriyan |
| 2024 | The Importance of Online Data: Understanding Preference Fine-tuning via Coverage. Yuda Song, Gokul Swamy, Aarti Singh, J. Andrew Bagnell, Wen Sun |
| 2024 | The Intelligible and Effective Graph Neural Additive Network. Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach |
| 2024 | The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information. Diyuan Wu, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, Dan Alistarh |
| 2024 | The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space. Hongyao Tang, Min Zhang, Chen Chen, Jianye Hao |
| 2024 | The Limits of Differential Privacy in Online Learning. Bo Li, Wei Wang, Peng Ye |
| 2024 | The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure. Tyler Sam, Yudong Chen, Christina Lee Yu |
| 2024 | The Mamba in the Llama: Distilling and Accelerating Hybrid Models. Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao |
| 2024 | The Many Faces of Optimal Weak-to-Strong Learning. Mikael Møller Høgsgaard, Kasper Green Larsen, Markus Engelund Mathiasen |
| 2024 | The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks. Christopher Blöcker, Chester Tan, Ingo Scholtes |
| 2024 | The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels. Florian Kalinke, Zoltán Szabó |
| 2024 | The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. Eirini Angeloudi, Jeroen Audenaert, Micah Bowles, Benjamin M. Boyd, David Chemaly, Brian Cherinka, Ioana Ciuca, Miles D. Cranmer, Aaron Do, Matthew Grayling, Erin E. Hayes, Tom Hehir, Shirley Ho, Marc Huertas-Company, Kartheik Iyer, Maja Jablonska, François Lanusse, Henry Leung, Kaisey Mandel, Rafael Martínez-Galarza, Peter Melchior, Lucas Meyer, Liam Holden Parker, Helen Qu, Jeff Shen, Michael J. Smith, Connor Stone, Mike Walmsley, John F. Wu |
| 2024 | The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models. Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew M. Bean, Katerina Margatina, Rafael Mosquera Gómez, Juan Ciro, Max Bartolo, Adina Williams, He He, Bertie Vidgen, Scott Hale |
| 2024 | The Poisson Midpoint Method for Langevin Dynamics: Provably Efficient Discretization for Diffusion Models. Saravanan Kandasamy, Dheeraj Nagaraj |
| 2024 | The Power of Extrapolation in Federated Learning. Hanmin Li, Kirill Acharya, Peter Richtárik |
| 2024 | The Power of Hard Attention Transformers on Data Sequences: A formal language theoretic perspective. Pascal Bergsträßer, Chris Köcher, Anthony Widjaja Lin, Georg Zetzsche |
| 2024 | The Power of Resets in Online Reinforcement Learning. Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin |
| 2024 | The Prevalence of Neural Collapse in Neural Multivariate Regression. George Andriopoulos, Zixuan Dong, Li Guo, Zifan Zhao, Keith W. Ross |
| 2024 | The Price of Implicit Bias in Adversarially Robust Generalization. Nikolaos Tsilivis, Natalie Frank, Nati Srebro, Julia Kempe |
| 2024 | The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems. Xiyuan Li, Youjun Wang, Weiwei Liu |
| 2024 | The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models. Diego Doimo, Alessandro Serra, Alessio Ansuini, Alberto Cazzaniga |
| 2024 | The Road Less Scheduled. Aaron Defazio, Xingyu Yang, Ahmed Khaled, Konstantin Mishchenko, Harsh Mehta, Ashok Cutkosky |
| 2024 | The Sample Complexity of Gradient Descent in Stochastic Convex Optimization. Roi Livni |
| 2024 | The Sample-Communication Complexity Trade-off in Federated Q-Learning. Sudeep Salgia, Yuejie Chi |
| 2024 | The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding. Kenneth C. Enevoldsen, Márton Kardos, Niklas Muennighoff, Kristoffer L. Nielbo |
| 2024 | The Secretary Problem with Predicted Additive Gap. Alexander Braun, Sherry Sarkar |
| 2024 | The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks. Simon Mataigne, Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane |
| 2024 | The Space Complexity of Approximating Logistic Loss. Gregory Dexter, Petros Drineas, Rajiv Khanna |
| 2024 | The Star Geometry of Critic-Based Regularizer Learning. Oscar Leong, Eliza O'Reilly, Yong Sheng Soh |
| 2024 | The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track. Eshta Bhardwaj, Harshit Gujral, Siyi Wu, Ciara Zogheib, Tegan Maharaj, Christoph Becker |
| 2024 | The Surprising Effectiveness of SP Voting with Partial Preferences. Hadi Hosseini, Debmalya Mandal, Amrit Puhan |
| 2024 | The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning. Moritz Schneider, Robert Krug, Narunas Vaskevicius, Luigi Palmieri, Joschka Boedecker |
| 2024 | The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better. Scott Geng, Cheng-Yu Hsieh, Vivek Ramanujan, Matthew Wallingford, Chun-Liang Li, Pang Wei Koh, Ranjay Krishna |
| 2024 | The Value of Reward Lookahead in Reinforcement Learning. Nadav Merlis, Dorian Baudry, Vianney Perchet |
| 2024 | The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam Holden Parker, Miles D. Cranmer, Shirley Ho |
| 2024 | The iNaturalist Sounds Dataset. Mustafa Chasmai, Alexander Shepard, Subhransu Maji, Grant Van Horn |
| 2024 | The motion planning neural circuit in goal-directed navigation as Lie group operator search. Junfeng Zuo, Ying Nian Wu, Si Wu, Wenhao Zhang |
| 2024 | The surprising efficiency of temporal difference learning for rare event prediction. Xiaoou Cheng, Jonathan Weare |
| 2024 | The tree autoencoder model, with application to hierarchical data visualization. Miguel Á. Carreira-Perpiñán, Kuat Gazizov |
| 2024 | Theoretical Analysis of Weak-to-Strong Generalization. Hunter Lang, David A. Sontag, Aravindan Vijayaraghavan |
| 2024 | Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks. Jim Zhao, Sidak Pal Singh, Aurélien Lucchi |
| 2024 | Theoretical Foundations of Deep Selective State-Space Models. Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker, Cristopher Salvi, Terry J. Lyons |
| 2024 | Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning. Yiqin Lv, Qi Wang, Dong Liang, Zheng Xie |
| 2024 | Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks. Arjun Subramonian, Jian Kang, Yizhou Sun |
| 2024 | Theoretical guarantees in KL for Diffusion Flow Matching. Marta Gentiloni Silveri, Alain Durmus, Giovanni Conforti |
| 2024 | Thinking Forward: Memory-Efficient Federated Finetuning of Language Models. Kunjal Panchal, Nisarg Parikh, Sunav Choudhary, Lijun Zhang, Yuriy Brun, Hui Guan |
| 2024 | This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization. Anthony Bardou, Patrick Thiran, Giovanni Ranieri |
| 2024 | Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox. Raymond Zhang, Richard Combes |
| 2024 | Thought of Search: Planning with Language Models Through The Lens of Efficiency. Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi |
| 2024 | Tight Bounds for Learning RUMs from Small Slates. Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins |
| 2024 | Tight Rates for Bandit Control Beyond Quadratics. Y. Jennifer Sun, Zhou Lu |
| 2024 | Tighter Convergence Bounds for Shuffled SGD via Primal-Dual Perspective. Xufeng Cai, Cheuk Yin Lin, Jelena Diakonikolas |
| 2024 | Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences. Zhaoze Wang, Ronald W. Di Tullio, Spencer Rooke, Vijay Balasubramanian |
| 2024 | Time-Constrained Robust MDPs. Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, Emmanuel Rachelson |
| 2024 | Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting. Qingxiang Liu, Xu Liu, Chenghao Liu, Qingsong Wen, Yuxuan Liang |
| 2024 | Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis. Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B. Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, B. Aditya Prakash |
| 2024 | Time-Reversal Provides Unsupervised Feedback to LLMs. Yerram Varun, Rahul Madhavan, Sravanti Addepalli, Arun Suggala, Karthikeyan Shanmugam, Prateek Jain |
| 2024 | Time-Varying LoRA: Towards Effective Cross-Domain Fine-Tuning of Diffusion Models. Zhan Zhuang, Yulong Zhang, Xuehao Wang, Jiangang Lu, Ying Wei, Yu Zhang |
| 2024 | TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables. Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Guo Qin, Haoran Zhang, Yong Liu, Yunzhong Qiu, Jianmin Wang, Mingsheng Long |
| 2024 | Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. Vijay Ekambaram, Arindam Jati, Pankaj Dayama, Sumanta Mukherjee, Nam Nguyen, Wesley M. Gifford, Chandra Reddy, Jayant Kalagnanam |
| 2024 | TinyLUT: Tiny Look-Up Table for Efficient Image Restoration at the Edge. Huanan Li, Juntao Guan, Lai Rui, Sijun Ma, Lin Gu, Noperson |
| 2024 | TinyTTA: Efficient Test-time Adaptation via Early-exit Ensembles on Edge Devices. Hong Jia, Young D. Kwon, Alessio Orsino, Ting Dang, Domenico Talia, Cecilia Mascolo |
| 2024 | To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty. Yasin Abbasi-Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári |
| 2024 | To Err Like Human: Affective Bias-Inspired Measures for Visual Emotion Recognition Evaluation. Chenxi Zhao, Jinglei Shi, Liqiang Nie, Jufeng Yang |
| 2024 | To Learn or Not to Learn, That is the Question - A Feature-Task Dual Learning Model of Perceptual Learning. Xiao Liu, Muyang Lyu, Cong Yu, Si Wu |
| 2024 | Token Merging for Training-Free Semantic Binding in Text-to-Image Synthesis. Taihang Hu, Linxuan Li, Joost van de Weijer, Hongcheng Gao, Fahad Shahbaz Khan, Jian Yang, Ming-Ming Cheng, Kai Wang, Yaxing Wang |
| 2024 | Tolerant Algorithms for Learning with Arbitrary Covariate Shift. Surbhi Goel, Abhishek Shetty, Konstantinos Stavropoulos, Arsen Vasilyan |
| 2024 | Topic-Conversation Relevance (TCR) Dataset and Benchmarks. Yaran Fan, Jamie Pool, Senja Filipi, Ross Cutler |
| 2024 | TopoFR: A Closer Look at Topology Alignment on Face Recognition. Jun Dan, Yang Liu, Jiankang Deng, Haoyu Xie, Siyuan Li, Baigui Sun, Shan Luo |
| 2024 | TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes. Yanping Fu, Wenbin Liao, Xinyuan Liu, Hang Xu, Yike Ma, Yucheng Zhang, Feng Dai |
| 2024 | Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms. Rayna Andreeva, Benjamin Dupuis, Rik Sarkar, Tolga Birdal, Umut Simsekli |
| 2024 | Topological obstruction to the training of shallow ReLU neural networks. Marco Nurisso, Pierrick Leroy, Francesco Vaccarino |
| 2024 | TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning. Nemin Wu, Qian Cao, Zhangyu Wang, Zeping Liu, Yanlin Qi, Jielu Zhang, Joshua Ni, Xiaobai Angela Yao, Hongxu Ma, Lan Mu, Stefano Ermon, Tanuja Ganu, Akshay Nambi, Ni Lao, Gengchen Mai |
| 2024 | Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation? Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang, Fabian Isensee, Zifu Wang, Jieneng Chen, Yu-Cheng Chou, Yannick Kirchhoff, Maximilian Rokuss, Ziyan Huang, Jin Ye, Junjun He, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus H. Maier-Hein, Paul F. Jaeger, Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia, Zhaohu Xing, Lei Zhu, Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari, Reza Azad, Dorit Merhof, Pengcheng Shi, Ting Ma, Yuxin Du, Fan Bai, Tiejun Huang, Bo Zhao, Haonan Wang, Xiaomeng Li, Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski, Saumya Gupta, Linshan Wu, Jia-Xin Zhuang, Hao Chen, Holger Roth, Daguang Xu, Matthew B. Blaschko, Sergio Decherchi, Andrea Cavalli, Alan L. Yuille, Zongwei Zhou |
| 2024 | Toward Approaches to Scalability in 3D Human Pose Estimation. Jun-Hui Kim, Seong-Whan Lee |
| 2024 | Toward Conditional Distribution Calibration in Survival Prediction. Shiang Qi, Yakun Yu, Russell Greiner |
| 2024 | Toward Dynamic Non-Line-of-Sight Imaging with Mamba Enforced Temporal Consistency. Yue Li, Yi Sun, Shida Sun, Juntian Ye, Yueyi Zhang, Feihu Xu, Zhiwei Xiong |
| 2024 | Toward Efficient Inference for Mixture of Experts. Haiyang Huang, Newsha Ardalani, Anna Y. Sun, Liu Ke, Shruti Bhosale, Hsien-Hsin S. Lee, Carole-Jean Wu, Benjamin Lee |
| 2024 | Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models. Weihang Xu, Maryam Fazel, Simon S. Du |
| 2024 | Toward Real Ultra Image Segmentation: Leveraging Surrounding Context to Cultivate General Segmentation Model. Sai Wang, Yutian Lin, Yu Wu, Bo Du |
| 2024 | Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning. Mingcheng Li, Dingkang Yang, Yang Liu, Shunli Wang, Jiawei Chen, Shuaibing Wang, Jinjie Wei, Yue Jiang, Qingyao Xu, Xiaolu Hou, Mingyang Sun, Ziyun Qian, Dongliang Kou, Lihua Zhang |
| 2024 | Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing. Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Lei Han, Haitao Mi, Dong Yu |
| 2024 | Toward Semantic Gaze Target Detection. Samy Tafasca, Anshul Gupta, Victor Bros, Jean-Marc Odobez |
| 2024 | Toward a Stable, Fair, and Comprehensive Evaluation of Object Hallucination in Large Vision-Language Models. Hongliang Wei, Xingtao Wang, Xianqi Zhang, Xiaopeng Fan, Debin Zhao |
| 2024 | Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning. Dongjoon Lee, Hyeryn Park, Changhee Lee |
| 2024 | Towards Accurate and Fair Cognitive Diagnosis via Monotonic Data Augmentation. Zheng Zhang, Wei Song, Qi Liu, Qingyang Mao, Yiyan Wang, Weibo Gao, Zhenya Huang, Shijin Wang, Enhong Chen |
| 2024 | Towards Calibrated Robust Fine-Tuning of Vision-Language Models. Changdae Oh, Hyesu Lim, Mijoo Kim, Dongyoon Han, Sangdoo Yun, Jaegul Choo, Alexander Hauptmann, Zhi-Qi Cheng, Kyungwoo Song |
| 2024 | Towards Combating Frequency Simplicity-biased Learning for Domain Generalization. Xilin He, Jingyu Hu, Qinliang Lin, Cheng Luo, Weicheng Xie, Siyang Song, Muhammad Haris Khan, Linlin Shen |
| 2024 | Towards Comprehensive Detection of Chinese Harmful Memes. Junyu Lu, Bo Xu, Xiaokun Zhang, Hongbo Wang, Haohao Zhu, Dongyu Zhang, Liang Yang, Hongfei Lin |
| 2024 | Towards Croppable Implicit Neural Representations. Maor Ashkenazi, Eran Treister |
| 2024 | Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang, Chen Chen, Bill Lin |
| 2024 | Towards Dynamic Message Passing on Graphs. Junshu Sun, Chenxue Yang, Xiangyang Ji, Qingming Huang, Shuhui Wang |
| 2024 | Towards Editing Time Series. Baoyu Jing, Shuqi Gu, Tianyu Chen, Zhiyu Yang, Dongsheng Li, Jingrui He, Kan Ren |
| 2024 | Towards Effective Planning Strategies for Dynamic Opinion Networks. Bharath Muppasani, Protik Nag, Vignesh Narayanan, Biplav Srivastava, Michael N. Huhns |
| 2024 | Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits. Julien Zhou, Pierre Gaillard, Thibaud Rahier, Houssam Zenati, Julyan Arbel |
| 2024 | Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding. Alexis Bellot, Silvia Chiappa |
| 2024 | Towards Exact Gradient-based Training on Analog In-memory Computing. Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen |
| 2024 | Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection. Chaoda Zheng, Feng Wang, Naiyan Wang, Shuguang Cui, Zhen Li |
| 2024 | Towards Flexible Visual Relationship Segmentation. Fangrui Zhu, Jianwei Yang, Huaizu Jiang |
| 2024 | Towards General Loop Invariant Generation: A Benchmark of Programs with Memory Manipulation. Chang Liu, Xiwei Wu, Yuan Feng, Qinxiang Cao, Junchi Yan |
| 2024 | Towards Global Optimal Visual In-Context Learning Prompt Selection. Chengming Xu, Chen Liu, Yikai Wang, Yuan Yao, Yanwei Fu |
| 2024 | Towards Harmless Rawlsian Fairness Regardless of Demographic Prior. Xuanqian Wang, Jing Li, Ivor W. Tsang, Yew Soon Ong |
| 2024 | Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox. Haohui Wang, Weijie Guan, Jianpeng Chen, Zi Wang, Dawei Zhou |
| 2024 | Towards Human-AI Complementarity with Prediction Sets. Giovanni De Toni, Nastaran Okati, Suhas Thejaswi, Eleni Straitouri, Manuel Gomez Rodriguez |
| 2024 | Towards Learning Group-Equivariant Features for Domain Adaptive 3D Detection. Sangyun Shin, Yuhang He, Madhu Vankadari, Ta Ying Cheng, Qian Xie, Andrew Markham, Niki Trigoni |
| 2024 | Towards Multi-Domain Learning for Generalizable Video Anomaly Detection. MyeongAh Cho, Taeoh Kim, Minho Shim, Dongyoon Wee, Sangyoun Lee |
| 2024 | Towards Multi-dimensional Explanation Alignment for Medical Classification. Lijie Hu, Songning Lai, Wenshuo Chen, Hongru Xiao, Hongbin Lin, Lu Yu, Jingfeng Zhang, Di Wang |
| 2024 | Towards Neuron Attributions in Multi-Modal Large Language Models. Junfeng Fang, Zac Bi, Ruipeng Wang, Houcheng Jiang, Yuan Gao, Kun Wang, An Zhang, Jie Shi, Xiang Wang, Tat-Seng Chua |
| 2024 | Towards Next-Generation Logic Synthesis: A Scalable Neural Circuit Generation Framework. Zhihai Wang, Jie Wang, Qingyue Yang, Yinqi Bai, Xing Li, Lei Chen, Jianye Hao, Mingxuan Yuan, Bin Li, Yongdong Zhang, Feng Wu |
| 2024 | Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers. Junhan Kim, Chungman Lee, Eulrang Cho, Kyungphil Park, Ho-Young Kim, Joonyoung Kim, Yongkweon Jeon |
| 2024 | Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking. Yuwei Zhang, Tong Xia, Jing Han, Yu Wu, Georgios Rizos, Yang Liu, Mohammed Mosuily, Jagmohan Chauhan, Cecilia Mascolo |
| 2024 | Towards Open-Vocabulary Semantic Segmentation Without Semantic Labels. Heeseong Shin, Chaehyun Kim, Sunghwan Hong, Seokju Cho, Anurag Arnab, Paul Hongsuck Seo, Seungryong Kim |
| 2024 | Towards Principled Graph Transformers. Luis Müller, Daniel Kusuma, Blai Bonet, Christopher Morris |
| 2024 | Towards Reliable Model Selection for Unsupervised Domain Adaptation: An Empirical Study and A Certified Baseline. Dapeng Hu, Romy Luo, Jian Liang, Chuan Sheng Foo |
| 2024 | Towards Robust Multimodal Sentiment Analysis with Incomplete Data. Haoyu Zhang, Wenbin Wang, Tianshu Yu |
| 2024 | Towards Safe Concept Transfer of Multi-Modal Diffusion via Causal Representation Editing. Peiran Dong, Bingjie Wang, Song Guo, Junxiao Wang, Jie Zhang, Zicong Hong |
| 2024 | Towards Scalable and Stable Parallelization of Nonlinear RNNs. Xavier Gonzalez, Andrew Warrington, Jimmy T. H. Smith, Scott W. Linderman |
| 2024 | Towards Stable Representations for Protein Interface Prediction. Ziqi Gao, Zijing Liu, Yu Li, Jia Li |
| 2024 | Towards Understanding Evolving Patterns in Sequential Data. Qiuhao Zeng, Long-Kai Huang, Qi Chen, Charles X. Ling, Boyu Wang |
| 2024 | Towards Understanding Extrapolation: a Causal Lens. Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, Kun Zhang |
| 2024 | Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens. Ruifeng Ren, Yong Liu |
| 2024 | Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model. Mingyang Yi, Aoxue Li, Yi Xin, Zhenguo Li |
| 2024 | Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration. Kaihang Pan, Zhaoyu Fan, Juncheng Li, Qifan Yu, Hao Fei, Siliang Tang, Richang Hong, Hanwang Zhang, Qianru Sun |
| 2024 | Towards Universal Mesh Movement Networks. Mingrui Zhang, Chunyang Wang, Stephan C. Kramer, Joseph G. Wallwork, Siyi Li, Jiancheng Liu, Xiang Chen, Matthew D. Piggott |
| 2024 | Towards Unsupervised Model Selection for Domain Adaptive Object Detection. Hengfu Yu, Jinhong Deng, Wen Li, Lixin Duan |
| 2024 | Towards Visual Text Design Transfer Across Languages. Yejin Choi, Jiwan Chung, Sumin Shim, Giyeong Oh, Youngjae Yu |
| 2024 | Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. Yizi Zhang, Yanchen Wang, Donato Jiménez-Benetó, Zixuan Wang, Mehdi Azabou, Blake A. Richards, Renee Tung, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz |
| 2024 | Towards a Scalable Reference-Free Evaluation of Generative Models. Azim Ospanov, Jingwei Zhang, Mohammad Jalali, Xuenan Cao, Andrej Bogdanov, Farzan Farnia |
| 2024 | Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics. Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael I. Jordan, Jiantao Jiao, Yuandong Tian, Stuart J. Russell |
| 2024 | Towards a theory of how the structure of language is acquired by deep neural networks. Francesco Cagnetta, Matthieu Wyart |
| 2024 | Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning. Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Yang Yu, Junqiao Zhao, Pheng-Ann Heng |
| 2024 | Towards the Dynamics of a DNN Learning Symbolic Interactions. Qihan Ren, Junpeng Zhang, Yang Xu, Yue Xin, Dongrui Liu, Quanshi Zhang |
| 2024 | Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning. Andreas Schlaginhaufen, Maryam Kamgarpour |
| 2024 | Towards training digitally-tied analog blocks via hybrid gradient computation. Timothy Nest, Maxence Ernoult |
| 2024 | Toxicity Detection for Free. Zhanhao Hu, Julien Piet, Geng Zhao, Jiantao Jiao, David A. Wagner |
| 2024 | TrAct: Making First-layer Pre-Activations Trainable. Felix Petersen, Christian Borgelt, Stefano Ermon |
| 2024 | Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs. Ching-An Cheng, Allen Nie, Adith Swaminathan |
| 2024 | Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network. Xiao Guo, Vishal Asnani, Sijia Liu, Xiaoming Liu |
| 2024 | TrackIME: Enhanced Video Point Tracking via Instance Motion Estimation. Seong Hyeon Park, Huiwon Jang, Byungwoo Jeon, Sukmin Yun, Paul Hongsuck Seo, Jinwoo Shin |
| 2024 | Trade-Offs of Diagonal Fisher Information Matrix Estimators. Alexander Soen, Ke Sun |
| 2024 | Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes. Spencer Rooke, Zhaoze Wang, Ronald W. Di Tullio, Vijay Balasubramanian |
| 2024 | Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification. Enrique B. Nueve, Dhamma Kimpara, Bo Waggoner, Jessica Finocchiaro |
| 2024 | Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning. Yeongbin Seo, Dongha Lee, Jinyoung Yeo |
| 2024 | Training Binary Neural Networks via Gaussian Variational Inference and Low-Rank Semidefinite Programming. Lorenzo Orecchia, Jiawei Hu, Xue He, Wang Mark, XuLei Yang, Min Wu, Xue Geng |
| 2024 | Training Compute-Optimal Protein Language Models. Xingyi Cheng, Bo Chen, Pan Li, Jing Gong, Jie Tang, Le Song |
| 2024 | Training Data Attribution via Approximate Unrolling. Juhan Bae, Wu Lin, Jonathan Lorraine, Roger B. Grosse |
| 2024 | Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis. Hongru Yang, Bhavya Kailkhura, Zhangyang Wang, Yingbin Liang |
| 2024 | Training an Open-Vocabulary Monocular 3D Detection Model without 3D Data. Rui Huang, Henry Zheng, Yan Wang, Zhuofan Xia, Marco Pavone, Gao Huang |
| 2024 | Training for Stable Explanation for Free. Chao Chen, Chenghua Guo, Rufeng Chen, Guixiang Ma, Ming Zeng, Xiangwen Liao, Xi Zhang, Sihong Xie |
| 2024 | Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy. Hancheng Ye, Jiakang Yuan, Renqiu Xia, Xiangchao Yan, Tao Chen, Junchi Yan, Botian Shi, Bo Zhang |
| 2024 | Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts. Zhiwei Lin, Yongtao Wang, Zhi Tang |
| 2024 | TrajCLIP: Pedestrian trajectory prediction method using contrastive learning and idempotent networks. Pengfei Yao, Yinglong Zhu, Huikun Bi, Tianlu Mao, Zhaoqi Wang |
| 2024 | Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q Volodymyr Tkachuk, Gellért Weisz, Csaba Szepesvári |
| 2024 | Trajectory Diffusion for ObjectGoal Navigation. Xinyao Yu, Sixian Zhang, Xinhang Song, Xiaorong Qin, Shuqiang Jiang |
| 2024 | Trajectory Flow Matching with Applications to Clinical Time Series Modelling. Xi Zhang, Yuan Pu, Yuki Kawamura, Andrew Loza, Yoshua Bengio, Dennis L. Shung, Alexander Tong |
| 2024 | Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning. Runqian Wang, Soumya Ghosh, David D. Cox, Diego Antognini, Aude Oliva, Rogério Feris, Leonid Karlinsky |
| 2024 | TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration. Yiwei Guo, Shaobin Zhuang, Kunchang Li, Yu Qiao, Yali Wang |
| 2024 | TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation. Chenyang Le, Yao Qian, Dongmei Wang, Long Zhou, Shujie Liu, Xiaofei Wang, Midia Yousefi, Yanmin Qian, Jinyu Li, Sheng Zhao, Michael Zeng |
| 2024 | Transcendence: Generative Models Can Outperform The Experts That Train Them. Edwin Zhang, Vincent Zhu, Naomi Saphra, Anat Kleiman, Benjamin L. Edelman, Milind Tambe, Sham M. Kakade, Eran Malach |
| 2024 | Transcoders find interpretable LLM feature circuits. Jacob Dunefsky, Philippe Chlenski, Neel Nanda |
| 2024 | Transductive Active Learning: Theory and Applications. Jonas Hübotter, Bhavya Sukhija, Lenart Treven, Yarden As, Andreas Krause |
| 2024 | Transductive Learning is Compact. Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng |
| 2024 | Transfer Learning for Diffusion Models. Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng |
| 2024 | Transfer Learning for Latent Variable Network Models. Akhil Jalan, Arya Mazumdar, Soumendu Sundar Mukherjee, Purnamrita Sarkar |
| 2024 | Transfer Q-star : Principled Decoding for LLM Alignment. Souradip Chakraborty, Soumya Suvra Ghosal, Ming Yin, Dinesh Manocha, Mengdi Wang, Amrit Singh Bedi, Furong Huang |
| 2024 | Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness. Mingyuan Fan, Xiaodan Li, Cen Chen, Wenmeng Zhou, Yaliang Li |
| 2024 | Transferable Adversarial Attacks on SAM and Its Downstream Models. Song Xia, Wenhan Yang, Yi Yu, Xun Lin, Henghui Ding, Lingyu Duan, Xudong Jiang |
| 2024 | Transferable Boltzmann Generators. Leon Klein, Frank Noé |
| 2024 | Transferring disentangled representations: bridging the gap between synthetic and real images. Jacopo Dapueto, Nicoletta Noceti, Francesca Odone |
| 2024 | Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization. Omar Montasser, Han Shao, Emmanuel Abbe |
| 2024 | Transformer Doctor: Diagnosing and Treating Vision Transformers. Jiacong Hu, Hao Chen, Kejia Chen, Yang Gao, Jingwen Ye, Xingen Wang, Mingli Song, Zunlei Feng |
| 2024 | Transformers Can Do Arithmetic with the Right Embeddings. Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein |
| 2024 | Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression. Deqing Fu, Tian-Qi Chen, Robin Jia, Vatsal Sharan |
| 2024 | Transformers Represent Belief State Geometry in their Residual Stream. Adam S. Shai, Lucas Teixeira, Alexander Gietelink Oldenziel, Sarah Marzen, Paul M. Riechers |
| 2024 | Transformers are Minimax Optimal Nonparametric In-Context Learners. Juno Kim, Tai Nakamaki, Taiji Suzuki |
| 2024 | Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models. Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen |
| 2024 | Transformers need glasses! Information over-squashing in language tasks. Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João Guilherme Madeira Araújo, Oleksandr Vitvitskyi, Razvan Pascanu, Petar Velickovic |
| 2024 | Transformers on Markov data: Constant depth suffices. Nived Rajaraman, Marco Bondaschi, Ashok Vardhan Makkuva, Kannan Ramchandran, Michael Gastpar |
| 2024 | Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models. Aviv Bick, Kevin Y. Li, Eric P. Xing, J. Zico Kolter, Albert Gu |
| 2024 | Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learner. Hanwen Zhong, Jiaxin Chen, Yutong Zhang, Di Huang, Yunhong Wang |
| 2024 | Transition Constrained Bayesian Optimization via Markov Decision Processes. Jose Pablo Folch, Calvin Tsay, Robert M. Lee, Behrang Shafei, Weronika Ormaniec, Andreas Krause, Mark van der Wilk, Ruth Misener, Mojmir Mutny |
| 2024 | Trap-MID: Trapdoor-based Defense against Model Inversion Attacks. Zhenting Liu, ShangTse Chen |
| 2024 | Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness. Václav Vorácek |
| 2024 | Tree of Attacks: Jailbreaking Black-Box LLMs Automatically. Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum S. Anderson, Yaron Singer, Amin Karbasi |
| 2024 | TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing. Junxi Xiao, Qinliang Su |
| 2024 | Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees. Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret, Alp Kucukelbir, David M. Blei |
| 2024 | Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization. Chengtao Jian, Kai Yang, Yang Jiao |
| 2024 | TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives. Maitreya Patel, Abhiram Kusumba, Sheng Cheng, Changhoon Kim, Tejas Gokhale, Chitta Baral, Yezhou Yang |
| 2024 | Truncated Variance Reduced Value Iteration. Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang |
| 2024 | Truth is Universal: Robust Detection of Lies in LLMs. Lennart Bürger, Fred A. Hamprecht, Boaz Nadler |
| 2024 | Truthful High Dimensional Sparse Linear Regression. Liyang Zhu, Amina Manseur, Meng Ding, Jinyan Liu, Jinhui Xu, Di Wang |
| 2024 | Truthfulness of Calibration Measures. Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao |
| 2024 | TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. Benjamin Feuer, Robin Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White |
| 2024 | TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models. Kiwoong Yoo, Owen Oertell, Junhyun Lee, Sanghoon Lee, Jaewoo Kang |
| 2024 | Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging. Zhenyi Lu, Chenghao Fan, Wei Wei, Xiaoye Qu, Dangyang Chen, Yu Cheng |
| 2024 | Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning. Shuguang Yu, Shuxing Fang, Ruixin Peng, Zhengling Qi, Fan Zhou, Chengchun Shi |
| 2024 | Typicalness-Aware Learning for Failure Detection. Yijun Liu, Jiequan Cui, Zhuotao Tian, Senqiao Yang, Qingdong He, Xiaoling Wang, Jingyong Su |
| 2024 | U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers. Yuchuan Tian, Zhijun Tu, Hanting Chen, Jie Hu, Chao Xu, Yunhe Wang |
| 2024 | UAV3D: A Large-scale 3D Perception Benchmark for Unmanned Aerial Vehicles. Hui Ye, Rajshekhar Sunderraman, Shihao Ji |
| 2024 | UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-World Document Analysis. Yulong Hui, Yao Lu, Huanchen Zhang |
| 2024 | UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems. Zhi Zheng, Changliang Zhou, Xialiang Tong, Mingxuan Yuan, Zhenkun Wang |
| 2024 | UDON: Universal Dynamic Online distillatioN for generic image representations. Nikolaos-Antonios Ypsilantis, Kaifeng Chen, André Araújo, Ondrej Chum |
| 2024 | UDPM: Upsampling Diffusion Probabilistic Models. Shady Abu-Hussein, Raja Giryes |
| 2024 | UGC: Universal Graph Coarsening. Mohit Kataria, Sandeep Kumar, Jayadeva |
| 2024 | UKnow: A Unified Knowledge Protocol with Multimodal Knowledge Graph Datasets for Reasoning and Vision-Language Pre-Training. Biao Gong, Shuai Tan, Yutong Feng, Xiaoying Xie, Yuyuan Li, Chaochao Chen, Kecheng Zheng, Yujun Shen, Deli Zhao |
| 2024 | UMB: Understanding Model Behavior for Open-World Object Detection. Xing Xi, Yangyang Huang, Zhijie Zhong, Ronghua Luo |
| 2024 | UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models. Jiachen Liang, Ruibing Hou, Minyang Hu, Hong Chang, Shiguang Shan, Xilin Chen |
| 2024 | UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes. Ted de Vries Lentsch, Holger Caesar, Dariu Gavrila |
| 2024 | UNIT: Unifying Image and Text Recognition in One Vision Encoder. Yi Zhu, Yanpeng Zhou, Chunwei Wang, Yang Cao, Jianhua Han, Lu Hou, Hang Xu |
| 2024 | UPS: Unified Projection Sharing for Lightweight Single-Image Super-resolution and Beyond. Kun Zhou, Xinyu Lin, Zhonghang Liu, Xiaoguang Han, Jiangbo Lu |
| 2024 | UQ-Guided Hyperparameter Optimization for Iterative Learners. Jiesong Liu, Feng Zhang, Jiawei Guan, Xipeng Shen |
| 2024 | UQE: A Query Engine for Unstructured Databases. Hanjun Dai, Bethany Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans |
| 2024 | USCILab3D: A Large-scale, Long-term, Semantically Annotated Outdoor Dataset. Kiran Lekkala, Henghui Bao, Peixu Cai, Wei Lim, Chen Liu, Laurent Itti |
| 2024 | UV-free Texture Generation with Denoising and Geodesic Heat Diffusion. Simone Foti, Stefanos Zafeiriou, Tolga Birdal |
| 2024 | UltraEdit: Instruction-based Fine-Grained Image Editing at Scale. Haozhe Zhao, Xiaojian (Shawn) Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, Baobao Chang |
| 2024 | UltraMedical: Building Specialized Generalists in Biomedicine. Kaiyan Zhang, Sihang Zeng, Ermo Hua, Ning Ding, Zhang-Ren Chen, Zhiyuan Ma, Haoxin Li, Ganqu Cui, Biqing Qi, Xuekai Zhu, Xingtai Lv, Jinfang Hu, Zhiyuan Liu, Bowen Zhou |
| 2024 | UltraPixel: Advancing Ultra High-Resolution Image Synthesis to New Peaks. Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, Lei Zhu |
| 2024 | Ultrafast classical phylogenetic method beats large protein language models on variant effect prediction. Sebastian Prillo, Wilson Wu, Yun Song |
| 2024 | UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation. Ye Sun, Hao Zhang, Tiehua Zhang, Xingjun Ma, Yu-Gang Jiang |
| 2024 | Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in LLMs. Zhiyuan Hu, Chumin Liu, Xidong Feng, Yilun Zhao, See-Kiong Ng, Anh Tuan Luu, Junxian He, Pang Wei W. Koh, Bryan Hooi |
| 2024 | Uncertainty-aware Fine-tuning of Segmentation Foundation Models. Kangning Liu, Brian L. Price, Jason Kuen, Yifei Fan, Zijun Wei, Luis Figueroa, Krzysztof J. Geras, Carlos Fernandez-Granda |
| 2024 | Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions. Rui Yang, Jie Wang, Guoping Wu, Bin Li |
| 2024 | Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast. Chufan Shi, Cheng Yang, Xinyu Zhu, Jiahao Wang, Taiqiang Wu, Siheng Li, Deng Cai, Yujiu Yang, Yu Meng |
| 2024 | Unconditional stability of a recurrent neural circuit implementing divisive normalization. Shivang Rawat, David J. Heeger, Stefano Martiniani |
| 2024 | Uncovering Safety Risks of Large Language Models through Concept Activation Vector. Zhihao Xu, Ruixuan Huang, Changyu Chen, Xiting Wang |
| 2024 | Uncovering the Redundancy in Graph Self-supervised Learning Models. Zhibiao Wang, Xiao Wang, Haoyue Deng, Nian Liu, Shirui Pan, Chunming Hu |
| 2024 | Uncovering, Explaining, and Mitigating the Superficial Safety of Backdoor Defense. Rui Min, Zeyu Qin, Nevin L. Zhang, Li Shen, Minhao Cheng |
| 2024 | Understanding Bias in Large-Scale Visual Datasets. Boya Zeng, Yida Yin, Zhuang Liu |
| 2024 | Understanding Emergent Abilities of Language Models from the Loss Perspective. Zhengxiao Du, Aohan Zeng, Yuxiao Dong, Jie Tang |
| 2024 | Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure. Xiang Li, Yixiang Dai, Qing Qu |
| 2024 | Understanding Hallucinations in Diffusion Models through Mode Interpolation. Sumukh K. Aithal, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter |
| 2024 | Understanding Information Storage and Transfer in Multi-Modal Large Language Models. Samyadeep Basu, Martin Grayson, Cecily Morrison, Besmira Nushi, Soheil Feizi, Daniela Massiceti |
| 2024 | Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective. Akiyoshi Tomihari, Issei Sato |
| 2024 | Understanding Model Selection for Learning in Strategic Environments. Tinashe Handina, Eric Mazumdar |
| 2024 | Understanding Multi-Granularity for Open-Vocabulary Part Segmentation. Jiho Choi, Seonho Lee, Seungho Lee, Minhyun Lee, Hyunjung Shim |
| 2024 | Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspective. Haixiang Sun, Ye Shi |
| 2024 | Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data. Alexander Havrilla, Wenjing Liao |
| 2024 | Understanding Transformer Reasoning Capabilities via Graph Algorithms. Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni |
| 2024 | Understanding Transformers via N-Gram Statistics. Timothy Nguyen |
| 2024 | Understanding Visual Feature Reliance through the Lens of Complexity. Thomas Fel, Louis Béthune, Andrew K. Lampinen, Thomas Serre, Katherine L. Hermann |
| 2024 | Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation. Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, Huawei Shen, Xueqi Cheng |
| 2024 | Understanding and Improving Training-free Loss-based Diffusion Guidance. Yifei Shen, Xinyang Jiang, Yifan Yang, Yezhen Wang, Dongqi Han, Dongsheng Li |
| 2024 | Understanding and Minimising Outlier Features in Transformer Training. Bobby He, Lorenzo Noci, Daniele Paliotta, Imanol Schlag, Thomas Hofmann |
| 2024 | Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks. Jerome Sieber, Carmen Amo Alonso, Alexandre Didier, Melanie N. Zeilinger, Antonio Orvieto |
| 2024 | Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling. Mingze Wang, Weinan E |
| 2024 | Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective. Takeshi Koshizuka, Masahiro Fujisawa, Yusuke Tanaka, Issei Sato |
| 2024 | Understanding the Gains from Repeated Self-Distillation. Divyansh Pareek, Simon S. Du, Sewoong Oh |
| 2024 | Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem. Declan Campbell, Sunayana Rane, Tyler Giallanza, Nicolò De Sabbata, Kia Ghods, Amogh Joshi, Alexander Ku, Steven Frankland, Tom Griffiths, Jonathan D. Cohen, Taylor W. Webb |
| 2024 | Understanding the Role of Equivariance in Self-supervised Learning. Yifei Wang, Kaiwen Hu, Sharut Gupta, Ziyu Ye, Yisen Wang, Stefanie Jegelka |
| 2024 | Understanding the Transferability of Representations via Task-Relatedness. Akshay Mehra, Yunbei Zhang, Jihun Hamm |
| 2024 | Unelicitable Backdoors via Cryptographic Transformer Circuits. Andis Draguns, Andrew Gritsevskiy, Sumeet Ramesh Motwani, Christian Schröder de Witt |
| 2024 | Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE. Xun Zhu, Ying Hu, Fanbin Mo, Miao Li, Ji Wu |
| 2024 | UniAR: A Unified model for predicting human Attention and Responses on visual content. Peizhao Li, Junfeng He, Gang Li, Rachit Bhargava, Shaolei Shen, Nachiappan Valliappan, Youwei Liang, Hongxiang Gu, Venky Ramachandran, Golnaz Farhadi, Yang Li, Kai Kohlhoff, Vidhya Navalpakkam |
| 2024 | UniAudio 1.5: Large Language Model-Driven Audio Codec is A Few-Shot Audio Task Learner. Dongchao Yang, Haohan Guo, Yuanyuan Wang, Rongjie Huang, Xiang Li, Xu Tan, Xixin Wu, Helen Meng |
| 2024 | UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling. Haider Al-Tahan, Quentin Garrido, Randall Balestriero, Diane Bouchacourt, Caner Hazirbas, Mark Ibrahim |
| 2024 | UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation. Hanzhang Zhou, Zijian Feng, Zixiao Zhu, Junlang Qian, Kezhi Mao |
| 2024 | UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior. Yao Wu, Mingwei Xing, Yachao Zhang, Xiaotong Luo, Yuan Xie, Yanyun Qu |
| 2024 | UniFL: Improve Latent Diffusion Model via Unified Feedback Learning. Jiacheng Zhang, Jie Wu, Yuxi Ren, Xin Xia, Huafeng Kuang, Pan Xie, Jiashi Li, Xuefeng Xiao, Weilin Huang, Shilei Wen, Lean Fu, Guanbin Li |
| 2024 | UniGAD: Unifying Multi-level Graph Anomaly Detection. Yiqing Lin, Jianheng Tang, Chenyi Zi, H. Vicky Zhao, Yuan Yao, Jia Li |
| 2024 | UniIF: Unified Molecule Inverse Folding. Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang, Siyuan Li, Zhirui Ye, Stan Z. Li |
| 2024 | UniMTS: Unified Pre-training for Motion Time Series. Xiyuan Zhang, Diyan Teng, Ranak Roy Chowdhury, Shuheng Li, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang |
| 2024 | UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections. Fangjinhua Wang, Marie-Julie Rakotosaona, Michael Niemeyer, Richard Szeliski, Marc Pollefeys, Federico Tombari |
| 2024 | UniTS: A Unified Multi-Task Time Series Model. Shanghua Gao, Teddy Koker, Owen Queen, Tom Hartvigsen, Theodoros Tsiligkaridis, Marinka Zitnik |
| 2024 | UniTox: Leveraging LLMs to Curate a Unified Dataset of Drug-Induced Toxicity from FDA Labels. Jacob Silberg, Kyle Swanson, Elana Simon, Angela Zhang, Zaniar Ghazizadeh, Scott Ogden, Hisham Hamadeh, James Y. Zou |
| 2024 | Unified Covariate Adjustment for Causal Inference. Yonghan Jung, Jin Tian, Elias Bareinboim |
| 2024 | Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection. Gyusam Chang, Jiwon Lee, Donghyun Kim, Jinkyu Kim, Dongwook Lee, Daehyun Ji, Sujin Jang, Sangpil Kim |
| 2024 | Unified Generative and Discriminative Training for Multi-modal Large Language Models. Wei Chow, Juncheng Li, Qifan Yu, Kaihang Pan, Hao Fei, Zhiqi Ge, Shuai Yang, Siliang Tang, Hanwang Zhang, Qianru Sun |
| 2024 | Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement. Zhehao Huang, Xinwen Cheng, JingHao Zheng, Haoran Wang, Zhengbao He, Tao Li, Xiaolin Huang |
| 2024 | Unified Graph Augmentations for Generalized Contrastive Learning on Graphs. Jiaming Zhuo, Yintong Lu, Hui Ning, Kun Fu, Bingxin Niu, Dongxiao He, Chuan Wang, Yuanfang Guo, Zhen Wang, Xiaochun Cao, Liang Yang |
| 2024 | Unified Guidance for Geometry-Conditioned Molecular Generation. Sirine Ayadi, Leon Hetzel, Johanna Sommer, Fabian J. Theis, Stephan Günnemann |
| 2024 | Unified Insights: Harnessing Multi-modal Data for Phenotype Imputation via View Decoupling. Qiannan Zhang, Weishen Pan, Zilong Bai, Chang Su, Fei Wang |
| 2024 | Unified Lexical Representation for Interpretable Visual-Language Alignment. Yifan Li, Yikai Wang, Yanwei Fu, Dongyu Ru, Zheng Zhang, Tong He |
| 2024 | Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification. Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann |
| 2024 | Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual Inputs. Alexandros Haliassos, Rodrigo Mira, Honglie Chen, Zoe Landgraf, Stavros Petridis, Maja Pantic |
| 2024 | Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning. Junyan Liu, Yunfan Li, Ruosong Wang, Lin Yang |
| 2024 | Unifying Generation and Prediction on Graphs with Latent Graph Diffusion. Cai Zhou, Xiyuan Wang, Muhan Zhang |
| 2024 | Unifying Homophily and Heterophily for Spectral Graph Neural Networks via Triple Filter Ensembles. Rui Duan, Mingjian Guang, Junli Wang, Chungang Yan, Hongda Qi, Wenkang Su, Can Tian, Haoran Yang |
| 2024 | Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image. Kailu Wu, Fangfu Liu, Zhihan Cai, Runjie Yan, Hanyang Wang, Yating Hu, Yueqi Duan, Kaisheng Ma |
| 2024 | Unitary Convolutions for Learning on Graphs and Groups. Bobak T. Kiani, Lukas Fesser, Melanie Weber |
| 2024 | United We Stand, Divided We Fall: Fingerprinting Deep Neural Networks via Adversarial Trajectories. Tianlong Xu, Chen Wang, Gaoyang Liu, Yang Yang, Kai Peng, Wei Liu |
| 2024 | Unity by Diversity: Improved Representation Learning for Multimodal VAEs. Thomas M. Sutter, Yang Meng, Andrea Agostini, Daphné Chopard, Norbert Fortin, Julia E. Vogt, Babak Shahbaba, Stephan Mandt |
| 2024 | Universal Exact Compression of Differentially Private Mechanisms. Yanxiao Liu, Wei-Ning Chen, Ayfer Özgür, Cheuk Ting Li |
| 2024 | Universal In-Context Approximation By Prompting Fully Recurrent Models. Aleksandar Petrov, Tom A. Lamb, Alasdair Paren, Philip Torr, Adel Bibi |
| 2024 | Universal Neural Functionals. Allan Zhou, Chelsea Finn, James Harrison |
| 2024 | Universal Online Convex Optimization with 1 Projection per Round. Wenhao Yang, Yibo Wang, Peng Zhao, Lijun Zhang |
| 2024 | Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators. Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter |
| 2024 | Universal Rates for Active Learning. Steve Hanneke, Amin Karbasi, Shay Moran, Grigoris Velegkas |
| 2024 | Universal Rates of Empirical Risk Minimization. Steve Hanneke, Mingyue Xu |
| 2024 | Universal Sample Coding. Szymon Kobus, Tze-Yang Tung, Deniz Gündüz |
| 2024 | Universality in Transfer Learning for Linear Models. Reza Ghane, Danil Akhtiamov, Babak Hassibi |
| 2024 | Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction. Anton Rodomanov, Xiaowen Jiang, Sebastian U. Stich |
| 2024 | UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models. Yihua Zhang, Chongyu Fan, Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jiancheng Liu, Gaoyuan Zhang, Gaowen Liu, Ramana Kompella, Xiaoming Liu, Sijia Liu |
| 2024 | Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need. Xianlong Wang, Minghui Li, Wei Liu, Hangtao Zhang, Shengshan Hu, Yechao Zhang, Ziqi Zhou, Hai Jin |
| 2024 | Unleashing Multispectral Video's Potential in Semantic Segmentation: A Semi-supervised Viewpoint and New UAV-View Benchmark. Wei Ji, Jingjing Li, Wenbo Li, Yilin Shen, Li Cheng, Hongxia Jin |
| 2024 | Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation. Yaoyuan Liang, Zhuojun Cai, Jian Xu, Guanbo Huang, Yiran Wang, Xiao Liang, Jiahao Liu, Ziran Li, Jingang Wang, Shao-Lun Huang |
| 2024 | Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems. Jiawei Zhang, Jiaxin Zhuang, Cheng Jin, Gen Li, Yuantao Gu |
| 2024 | Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation. Muzhi Zhu, Yang Liu, Zekai Luo, Chenchen Jing, Hao Chen, Guangkai Xu, Xinlong Wang, Chunhua Shen |
| 2024 | Unlock the Intermittent Control Ability of Model Free Reinforcement Learning. Jiashun Liu, Jianye Hao, Xiaotian Hao, Yi Ma, Yan Zheng, Yujing Hu, Tangjie Lv |
| 2024 | Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. Sanae Lotfi, Yilun Kuang, Marc Finzi, Brandon Amos, Micah Goldblum, Andrew Gordon Wilson |
| 2024 | Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance. Jiwan Hur, Dong-Jae Lee, Gyojin Han, Jaehyun Choi, Yunho Jeon, Junmo Kim |
| 2024 | Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought. Qiguang Chen, Libo Qin, Jiaqi Wang, Jingxuan Zhou, Wanxiang Che |
| 2024 | Unlocking the Potential of Global Human Expertise. Elliot Meyerson, Olivier Francon, Darren Sargent, Babak Hodjat, Risto Miikkulainen |
| 2024 | Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback. Hamish Ivison, Yizhong Wang, Jiacheng Liu, Zeqiu Wu, Valentina Pyatkin, Nathan Lambert, Noah A. Smith, Yejin Choi, Hanna Hajishirzi |
| 2024 | Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry. Marvin Alberts, Oliver Schilter, Federico Zipoli, Nina Hartrampf, Teodoro Laino |
| 2024 | Unraveling the Gradient Descent Dynamics of Transformers. Bingqing Song, Boran Han, Shuai Zhang, Jie Ding, Mingyi Hong |
| 2024 | Unravelling in Collaborative Learning. Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus |
| 2024 | Unrolled denoising networks provably learn to perform optimal Bayesian inference. Aayush Karan, Kulin Shah, Sitan Chen, Yonina C. Eldar |
| 2024 | Unscrambling disease progression at scale: fast inference of event permutations with optimal transport. Peter A. Wijeratne, Daniel C. Alexander |
| 2024 | Unsupervised Anomaly Detection in The Presence of Missing Values. Feng Xiao, Jicong Fan |
| 2024 | Unsupervised Discovery of Formulas for Mathematical Constants. Michael Shalyt, Uri Seligmann, Itay Beit Halachmi, Ofir David, Rotem Elimelech, Ido Kaminer |
| 2024 | Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization. Sanghyeob Song, Jaihyun Lew, Hyemi Jang, Sungroh Yoon |
| 2024 | Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation. Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang, Bo Li, Yang Tang, Pan Zhou |
| 2024 | Unsupervised Object Detection with Theoretical Guarantees. Marian Longa, João F. Henriques |
| 2024 | Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms. Mengyu Zhao, Xi Chen, Xin Yuan, Shirin Jalali |
| 2024 | Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization. Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie |
| 2024 | Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? Haoang Chi, He Li, Wenjing Yang, Feng Liu, Long Lan, Xiaoguang Ren, Tongliang Liu, Bo Han |
| 2024 | Unveiling Encoder-Free Vision-Language Models. Haiwen Diao, Yufeng Cui, Xiaotong Li, Yueze Wang, Huchuan Lu, Xinlong Wang |
| 2024 | Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers. Siyu Chen, Heejune Sheen, Tianhao Wang, Zhuoran Yang |
| 2024 | Unveiling LoRA Intrinsic Ranks via Salience Analysis. Wenjun Ke, Jiahao Wang, Peng Wang, Jiajun Liu, Dong Nie, Guozheng Li, Yining Li |
| 2024 | Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance. Yiting Chen, Jiazi Bu, Junchi Yan |
| 2024 | Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms. Fan Yao, Yiming Liao, Jingzhou Liu, Shaoliang Nie, Qifan Wang, Haifeng Xu, Hongning Wang |
| 2024 | Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness. Weilin Lin, Li Liu, Shaokui Wei, Jianze Li, Hui Xiong |
| 2024 | Unveiling the Bias Impact on Symmetric Moral Consistency of Large Language Models. Ziyi Zhou, Xinwei Guo, Jiashi Gao, Xiangyu Zhao, Shiyao Zhang, Xin Yao, Xuetao Wei |
| 2024 | Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis. Rachel S. Y. Teo, Tan Nguyen |
| 2024 | Unveiling the Hidden: Online Vectorized HD Map Construction with Clip-Level Token Interaction and Propagation. Nayeon Kim, Hongje Seong, Daehyun Ji, Sujin Jang |
| 2024 | Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators. Yiyan Huang, Cheuk Hang Leung, Siyi Wang, Yijun Li, Qi Wu |
| 2024 | Unveiling the Tapestry of Consistency in Large Vision-Language Models. Yuan Zhang, Fei Xiao, Tao Huang, Chun-Kai Fan, Hongyuan Dong, Jiawen Li, Jiacong Wang, Kuan Cheng, Shanghang Zhang, Haoyuan Guo |
| 2024 | Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation. Xingru Huang, Yihao Guo, Jian Huang, Tianyun Zhang, Hong He, Shaowei Jiang, Yaoqi Sun |
| 2024 | UrbanDataLayer: A Unified Data Pipeline for Urban Science. Yiheng Wang, Tianyu Wang, Yuying Zhang, Hongji Zhang, Haoyu Zheng, Guanjie Zheng, Linghe Kong |
| 2024 | UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction. Yansong Ning, Hao Liu |
| 2024 | User-Creator Feature Polarization in Recommender Systems with Dual Influence. Tao Lin, Kun Jin, Andrew Estornell, Xiaoying Zhang, Yiling Chen, Yang Liu |
| 2024 | User-item fairness tradeoffs in recommendations. Sophie Greenwood, Sudalakshmee Chiniah, Nikhil Garg |
| 2024 | Using Noise to Infer Aspects of Simplicity Without Learning. Zachery Boner, Harry Chen, Lesia Semenova, Ronald Parr, Cynthia Rudin |
| 2024 | Using Surrogates in Covariate-adjusted Response-adaptive Randomization Experiments with Delayed Outcomes. Lei Shi, Waverly Wei, Jingshen Wang |
| 2024 | Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs. Franziska Heeg, Ingo Scholtes |
| 2024 | Using Unity to Help Solve Reinforcement Learning. Connor Brennan, Andrew Williams, Omar G. Younis, Vedant Vyas, Daria Yasafova, Irina Rish |
| 2024 | Utilizing Human Behavior Modeling to Manipulate Explanations in AI-Assisted Decision Making: The Good, the Bad, and the Scary. Zhuoyan Li, Ming Yin |
| 2024 | Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series. Ilan Naiman, Nimrod Berman, Itai Pemper, Idan Arbiv, Gal Fadlon, Omri Azencot |
| 2024 | V-PETL Bench: A Unified Visual Parameter-Efficient Transfer Learning Benchmark. Yi Xin, Siqi Luo, Xuyang Liu, Yuntao Du, Haodi Zhou, Xinyu Cheng, Christina E. Lee, Junlong Du, Haozhe Wang, Mingcai Chen, Ting Liu, Guimin Hu, Zhongwei Wan, Rongchao Zhang, Aoxue Li, Mingyang Yi, Xiaohong Liu |
| 2024 | VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time. Sicheng Xu, Guojun Chen, Yu-Xiao Guo, Jiaolong Yang, Chong Li, Zhenyu Zang, Yizhong Zhang, Xin Tong, Baining Guo |
| 2024 | VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks. Yang Li, Shaobo Han, Shihao Ji |
| 2024 | VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction. Hanlin Chen, Fangyin Wei, Chen Li, Tianxin Huang, Yunsong Wang, Gim Hee Lee |
| 2024 | VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding. Houlun Chen, Xin Wang, Hong Chen, Zeyang Zhang, Wei Feng, Bin Huang, Jia Jia, Wenwu Zhu |
| 2024 | VFIMamba: Video Frame Interpolation with State Space Models. Guozhen Zhang, Chunxu Liu, Yutao Cui, Xiaotong Zhao, Kai Ma, Limin Wang |
| 2024 | VHELM: A Holistic Evaluation of Vision Language Models. Tony Lee, Haoqin Tu, Chi Heem Wong, Wenhao Zheng, Yiyang Zhou, Yifan Mai, Josselin Somerville Roberts, Michihiro Yasunaga, Huaxiu Yao, Cihang Xie, Percy Liang |
| 2024 | VISA: Variational Inference with Sequential Sample-Average Approximations. Heiko Zimmermann, Christian Andersson Naesseth, Jan-Willem van de Meent |
| 2024 | VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance. Divyansh Srivastava, Ge Yan, Lily Weng |
| 2024 | VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark. Han Huang, Haitian Zhong, Tao Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan |
| 2024 | VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought. Gabriel Sarch, Lawrence Jang, Michael J. Tarr, William W. Cohen, Kenneth Marino, Katerina Fragkiadaki |
| 2024 | VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images. M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khurana, James P. Balhoff, Yasin Bakis, Bahadir Altintas, Matthew J. Thompson, Elizabeth G. Campolongo, Josef C. Uyeda, Hilmar Lapp, Henry L. Bart Jr., Paula M. Mabee, Yu Su, Wei-Lun Chao, Charles V. Stewart, Tanya Y. Berger-Wolf, Wasila M. Dahdul, Anuj Karpatne |
| 2024 | VLMimic: Vision Language Models are Visual Imitation Learner for Fine-grained Actions. Guangyan Chen, Meiling Wang, Te Cui, Yao Mu, Haoyang Lu, Tianxing Zhou, Zicai Peng, Mengxiao Hu, Haizhou Li, Li Yuan, Yi Yang, Yufeng Yue |
| 2024 | VMamba: Visual State Space Model. Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, Jianbin Jiao, Yunfan Liu |
| 2024 | VQ-Map: Bird's-Eye-View Map Layout Estimation in Tokenized Discrete Space via Vector Quantization. Yiwei Zhang, Jin Gao, Fudong Ge, Guan Luo, Bing Li, Zhao-Xiang Zhang, Haibin Ling, Weiming Hu |
| 2024 | VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. Xiang Li, Jian Ding, Mohamed Elhoseiny |
| 2024 | Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack. Tiansheng Huang, Sihao Hu, Ling Liu |
| 2024 | Validating Climate Models with Spherical Convolutional Wasserstein Distance. Robert C. Garrett, Trevor Harris, Zhuo Wang, Bo Li |
| 2024 | Value Imprint: A Technique for Auditing the Human Values Embedded in RLHF Datasets. Ike Obi, Rohan Pant, Srishti Shekhar Agrawal, Maham Ghazanfar, Aaron Basiletti |
| 2024 | Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training. Pihe Hu, Shaolong Li, Zhuoran Li, Ling Pan, Longbo Huang |
| 2024 | Variance estimation in compound decision theory under boundedness. Subhodh Kotekal |
| 2024 | Variational Delayed Policy Optimization. Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Chao Huang |
| 2024 | Variational Distillation of Diffusion Policies into Mixture of Experts. Hongyi Zhou, Denis Blessing, Ge Li, Onur Celik, Xiaogang Jia, Gerhard Neumann, Rudolf Lioutikov |
| 2024 | Variational Flow Matching for Graph Generation. Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth, Max Welling, Jan-Willem van de Meent |
| 2024 | Variational Multi-scale Representation for Estimating Uncertainty in 3D Gaussian Splatting. Ruiqi Li, Yiu-ming Cheung |
| 2024 | VastTrack: Vast Category Visual Object Tracking. Liang Peng, Junyuan Gao, Xinran Liu, Weihong Li, Shaohua Dong, Zhipeng Zhang, Heng Fan, Libo Zhang |
| 2024 | VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections. Roy Miles, Pradyumna Reddy, Ismail Elezi, Jiankang Deng |
| 2024 | VeXKD: The Versatile Integration of Cross-Modal Fusion and Knowledge Distillation for 3D Perception. Yuzhe Ji, Yijie Chen, Liuqing Yang, Rui Ding, Meng Yang, Xinhu Zheng |
| 2024 | Vector Quantization Prompting for Continual Learning. Li Jiao, Qiuxia Lai, Yu Li, Qiang Xu |
| 2024 | Verifiably Robust Conformal Prediction. Linus Jeary, Tom Kuipers, Mehran Hosseini, Nicola Paoletti |
| 2024 | Verified Code Transpilation with LLMs. Sahil Bhatia, Jie Qiu, Niranjan Hasabnis, Sanjit A. Seshia, Alvin Cheung |
| 2024 | Verified Safe Reinforcement Learning for Neural Network Dynamic Models. Junlin Wu, Huan Zhang, Yevgeniy Vorobeychik |
| 2024 | ViLCo-Bench: VIdeo Language COntinual learning Benchmark. Tianqi Tang, Shohreh Deldari, Hao Xue, Celso De Melo, Flora D. Salim |
| 2024 | VidMan: Exploiting Implicit Dynamics from Video Diffusion Model for Effective Robot Manipulation. Youpeng Wen, Junfan Lin, Yi Zhu, Jianhua Han, Hang Xu, Shen Zhao, Xiaodan Liang |
| 2024 | VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models. Wenhao Wang, Yi Yang |
| 2024 | Video Diffusion Models are Training-free Motion Interpreter and Controller. Zeqi Xiao, Yifan Zhou, Shuai Yang, Xingang Pan |
| 2024 | Video Token Merging for Long Video Understanding. Seon-Ho Lee, Jue Wang, Zhikang Zhang, David Fan, Xinyu Li |
| 2024 | VideoGUI: A Benchmark for GUI Automation from Instructional Videos. Kevin Qinghong Lin, Linjie Li, Difei Gao, Qinchen Wu, Mingyi Yan, Zhengyuan Yang, Lijuan Wang, Mike Zheng Shou |
| 2024 | VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation. Shiwei Wu, Joya Chen, Kevin Qinghong Lin, Qimeng Wang, Yan Gao, Qianli Xu, Tong Xu, Yao Hu, Enhong Chen, Mike Zheng Shou |
| 2024 | VideoTetris: Towards Compositional Text-to-Video Generation. Ye Tian, Ling Yang, Haotian Yang, Yuan Gao, Yufan Deng, Xintao Wang, Zhaochen Yu, Xin Tao, Pengfei Wan, Di Zhang, Bin Cui |
| 2024 | Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels. Yikai Wang, Xinzhou Wang, Zilong Chen, Zhengyi Wang, Fuchun Sun, Jun Zhu |
| 2024 | Virtual Scanning: Unsupervised Non-line-of-sight Imaging from Irregularly Undersampled Transients. Xingyu Cui, Huanjing Yue, Song Li, Xiangjun Yin, Yusen Hou, Yun Meng, Kai Zou, Xiaolong Hu, Jingyu Yang |
| 2024 | VisMin: Visual Minimal-Change Understanding. Rabiul Awal, Saba Ahmadi, Le Zhang, Aishwarya Agrawal |
| 2024 | Vision Foundation Model Enables Generalizable Object Pose Estimation. Kai Chen, Yiyao Ma, Xingyu Lin, Stephen James, Jianshu Zhou, Yun-Hui Liu, Pieter Abbeel, Qi Dou |
| 2024 | Vision Mamba Mender. Jiacong Hu, Anda Cao, Zunlei Feng, Shengxuming Zhang, Yi Wang, Lingxiang Jia, Mingli Song |
| 2024 | Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning. Chenyu Yang, Xizhou Zhu, Jinguo Zhu, Weijie Su, Junjie Wang, Xuan Dong, Wenhai Wang, Lewei Lu, Bin Li, Jie Zhou, Yu Qiao, Jifeng Dai |
| 2024 | Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights. Sy-Tuyen Ho, Tuan Van Vo, Somayeh Ebrahimkhani, Ngai-Man Cheung |
| 2024 | Vision-Language Models are Strong Noisy Label Detectors. Tong Wei, Hao-Tian Li, Chun-Shu Li, Jiang-Xin Shi, Yufeng Li, Min-Ling Zhang |
| 2024 | Vision-Language Navigation with Energy-Based Policy. Rui Liu, Wenguan Wang, Yi Yang |
| 2024 | VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks. Jiannan Wu, Muyan Zhong, Sen Xing, Zeqiang Lai, Zhaoyang Liu, Zhe Chen, Wenhai Wang, Xizhou Zhu, Lewei Lu, Tong Lu, Ping Luo, Yu Qiao, Jifeng Dai |
| 2024 | Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability. Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, Hongyang Li |
| 2024 | Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model. Haogeng Liu, Quanzeng You, Xiaotian Han, Yongfei Liu, Huaibo Huang, Ran He, Hongxia Yang |
| 2024 | Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction. Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, Liwei Wang |
| 2024 | Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning. Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, Hongsheng Li |
| 2024 | Visual Data Diagnosis and Debiasing with Concept Graphs. Rwiddhi Chakraborty, Yinong Wang, Jialu Gao, Runkai Zheng, Cheng Zhang, Fernando De la Torre |
| 2024 | Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion. Dongyang Li, Chen Wei, Shiying Li, Jiachen Zou, Quanying Liu |
| 2024 | Visual Fourier Prompt Tuning. Runjia Zeng, Cheng Han, Qifan Wang, Chunshu Wu, Tong Geng, Lifu Huang, Ying Nian Wu, Dongfang Liu |
| 2024 | Visual Perception by Large Language Model's Weights. Feipeng Ma, Hongwei Xue, Yizhou Zhou, Guangting Wang, Fengyun Rao, Shilin Yan, Yueyi Zhang, Siying Wu, Mike Zheng Shou, Xiaoyan Sun |
| 2024 | Visual Pinwheel Centers Act as Geometric Saliency Detectors. Haixin Zhong, Mingyi Huang, Wei Dai, Haoyu Wang, Anna Wang Roe, Yuguo Yu |
| 2024 | Visual Prompt Tuning in Null Space for Continual Learning. Yue Lu, Shizhou Zhang, De Cheng, Yinghui Xing, Nannan Wang, Peng Wang, Yanning Zhang |
| 2024 | Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models. Nitzan Bitton Guetta, Aviv Slobodkin, Aviya Maimon, Eliya Habba, Royi Rassin, Yonatan Bitton, Idan Szpektor, Amir Globerson, Yuval Elovici |
| 2024 | Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models. Yushi Hu, Weijia Shi, Xingyu Fu, Dan Roth, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Ranjay Krishna |
| 2024 | Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing. Hao Fei, Shengqiong Wu, Hanwang Zhang, Tat-Seng Chua, Shuicheng Yan |
| 2024 | Vivid-ZOO: Multi-View Video Generation with Diffusion Model. Bing Li, Cheng Zheng, Wenxuan Zhu, Jinjie Mai, Biao Zhang, Peter Wonka, Bernard Ghanem |
| 2024 | Vocal Call Locator Benchmark (VCL) for localizing rodent vocalizations from multi-channel audio. Ralph Peterson, Aramis Tanelus, Christopher Ick, Bartul Mimica, Niegil Francis Muttath Joseph, Violet Ivan, Aman Choudhri, Annegret Falkner, Mala Murthy, David M. Schneider, Dan Sanes, Alex Williams |
| 2024 | Voila-A: Aligning Vision-Language Models with User's Gaze Attention. Kun Yan, Zeyu Wang, Lei Ji, Yuntao Wang, Nan Duan, Shuai Ma |
| 2024 | Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection. Guowen Zhang, Lue Fan, Chenhang He, Zhen Lei, Zhaoxiang Zhang, Lei Zhang |
| 2024 | Voxel Proposal Network via Multi-Frame Knowledge Distillation for Semantic Scene Completion. Lubo Wang, Di Lin, Kairui Yang, Ruonan Liu, Qing Guo, Wuyuan Xie, Miaohui Wang, Lingyu Liang, Yi Wang, Ping Li |
| 2024 | Vript: A Video Is Worth Thousands of Words. Dongjie Yang, Suyuan Huang, Chengqiang Lu, Xiaodong Han, Haoxin Zhang, Yan Gao, Yao Hu, Hai Zhao |
| 2024 | WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models. Jinghan Jia, Jiancheng Liu, Yihua Zhang, Parikshit Ram, Nathalie Baracaldo, Sijia Liu |
| 2024 | WATT: Weight Average Test Time Adaptation of CLIP. David Osowiechi, Mehrdad Noori, Gustavo Adolfo Vargas Hakim, Moslem Yazdanpanah, Ali Bahri, Milad Cheraghalikhani, Sahar Dastani, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers |
| 2024 | WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control. Claire Bizon Monroc, Ana Busic, Donatien Dubuc, Jiamin Zhu |
| 2024 | WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models. Peng Wang, Zexi Li, Ningyu Zhang, Ziwen Xu, Yunzhi Yao, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen |
| 2024 | WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks. Michael Wornow, Avanika Narayan, Ben Viggiano, Ishan S. Khare, Tathagat Verma, Tibor Thompson, Miguel Angel Fuentes Hernandez, Sudharsan Sundar, Chloe Trujillo, Krrish Chawla, Rongfei Lu, Justin Shen, Divya Nagaraj, Joshua Martinez, Vardhan Agrawal, Althea Hudson, Nigam Shah, Christopher Ré |
| 2024 | Warm-starting Push-Relabel. Sami Davies, Sergei Vassilvitskii, Yuyan Wang |
| 2024 | Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes. Asaf Cassel, Aviv Rosenberg |
| 2024 | Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models. Giannis Daras, Weili Nie, Karsten Kreis, Alex Dimakis, Morteza Mardani, Nikola B. Kovachki, Arash Vahdat |
| 2024 | Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation. Jiaming Lv, Haoyuan Yang, Peihua Li |
| 2024 | Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness. Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi |
| 2024 | Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning. Takuo Matsubara |
| 2024 | Wasserstein convergence of Cech persistence diagrams for samplings of submanifolds. Charles Arnal, David Cohen-Steiner, Vincent Divol |
| 2024 | Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents. Wenkai Yang, Xiaohan Bi, Yankai Lin, Sishuo Chen, Jie Zhou, Xu Sun |
| 2024 | WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off. Eva Giboulot, Teddy Furon |
| 2024 | Watermarking Makes Language Models Radioactive. Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon |
| 2024 | WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks. Jun Xia, Zhihao Yue, Yingbo Zhou, Zhiwei Ling, Yiyu Shi, Xian Wei, Mingsong Chen |
| 2024 | Weak Supervision Performance Evaluation via Partial Identification. Felipe Maia Polo, Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun |
| 2024 | Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles. Qi Chen, Bowen Zhang, Gang Wang, Qi Wu |
| 2024 | Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models. Zhanhui Zhou, Zhixuan Liu, Jie Liu, Zhichen Dong, Chao Yang, Yu Qiao |
| 2024 | Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach. Mathilde Caron, Alireza Fathi, Cordelia Schmid, Ahmet Iscen |
| 2024 | Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs. Sukmin Yun, Haokun Lin, Rusiru Thushara, Mohammad Qazim Bhat, Yongxin Wang, Zutao Jiang, Mingkai Deng, Jinhong Wang, Tianhua Tao, Junbo Li, Haonan Li, Preslav Nakov, Timothy Baldwin, Zhengzhong Liu, Eric P. Xing, Xiaodan Liang, Zhiqiang Shen |
| 2024 | WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark. Chunhui Zhang, Li Liu, Guanjie Huang, Hao Wen, Xi Zhou, Yanfeng Wang |
| 2024 | WeiPer: OOD Detection using Weight Perturbations of Class Projections. Maximilian Granz, Manuel Heurich, Tim Landgraf |
| 2024 | Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments. Mixue Xie, Shuang Li, Binhui Xie, Chi Harold Liu, Jian Liang, Zixun Sun, Ke Feng, Chengwei Zhu |
| 2024 | Weight decay induces low-rank attention layers. Seijin Kobayashi, Yassir Akram, Johannes von Oswald |
| 2024 | Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML. Tehila Dahan, Kfir Y. Levy |
| 2024 | Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning. Raffaele Paolino, Sohir Maskey, Pascal Welke, Gitta Kutyniok |
| 2024 | WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking. Yunchao Liu, Ha Dong, Xin Wang, Rocco Moretti, Yu Wang, Zhaoqian Su, Jiawei Gu, Bobby Bodenheimer, Charles David Weaver, Jens Meiler, Tyler Derr |
| 2024 | WenMind: A Comprehensive Benchmark for Evaluating Large Language Models in Chinese Classical Literature and Language Arts. Jiahuan Cao, Yang Liu, Yongxin Shi, Kai Ding, Lianwen Jin |
| 2024 | What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration. Libo Qin, Qiguang Chen, Hao Fei, Zhi Chen, Min Li, Wanxiang Che |
| 2024 | What If the Input is Expanded in OOD Detection? Boxuan Zhang, Jianing Zhu, Zengmao Wang, Tongliang Liu, Bo Du, Bo Han |
| 2024 | What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks. Yilun Zheng, Sitao Luan, Lihui Chen |
| 2024 | What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights. Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, Xiaojuan Qi |
| 2024 | What Makes Partial-Label Learning Algorithms Effective? Jiaqi Lv, Yangfan Liu, Shiyu Xia, Ning Xu, Miao Xu, Gang Niu, Min-Ling Zhang, Masashi Sugiyama, Xin Geng |
| 2024 | What Makes and Breaks Safety Fine-tuning? A Mechanistic Study. Samyak Jain, Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip Torr, Amartya Sanyal, Puneet K. Dokania |
| 2024 | What Matters in Graph Class Incremental Learning? An Information Preservation Perspective. Jialu Li, Yu Wang, Pengfei Zhu, Wanyu Lin, Qinghua Hu |
| 2024 | What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information. Yiting Chen, Junchi Yan |
| 2024 | What Variables Affect Out-of-Distribution Generalization in Pretrained Models? Md Yousuf Harun, Kyungbok Lee, Gianmarco J. Gallardo, Giri Krishnan, Christopher Kanan |
| 2024 | What do Graph Neural Networks learn? Insights from Tropical Geometry. Tuan Anh Pham, Vikas Garg |
| 2024 | What does guidance do? A fine-grained analysis in a simple setting. Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu |
| 2024 | What is my quantum computer good for? Quantum capability learning with physics-aware neural networks. Daniel Hothem, Ashe Miller, Timothy Proctor |
| 2024 | What makes unlearning hard and what to do about it. Kairan Zhao, Meghdad Kurmanji, George-Octavian Barbulescu, Eleni Triantafillou, Peter Triantafillou |
| 2024 | What matters when building vision-language models? Hugo Laurençon, Léo Tronchon, Matthieu Cord, Victor Sanh |
| 2024 | What to Say and When to Say it: Live Fitness Coaching as a Testbed for Situated Interaction. Sunny Panchal, Apratim Bhattacharyya, Guillaume Berger, Antoine Mercier, Cornelius Böhm, Florian Dietrichkeit, Reza Pourreza, Xuanlin Li, Pulkit Madan, Mingu Lee, Mark Todorovich, Ingo Bax, Roland Memisevic |
| 2024 | What type of inference is planning? Miguel Lázaro-Gredilla, Li Yang Ku, Kevin P. Murphy, Dileep George |
| 2024 | When Is Inductive Inference Possible? Zhou Lu |
| 2024 | When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search. Xuan Chen, Yuzhou Nie, Wenbo Guo, Xiangyu Zhang |
| 2024 | When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models. Yinghui Li, Qingyu Zhou, Yuanzhen Luo, Shirong Ma, Yangning Li, Hai-Tao Zheng, Xuming Hu, Philip S. Yu |
| 2024 | When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback. Leon Lang, Davis Foote, Stuart J. Russell, Anca D. Dragan, Erik Jenner, Scott Emmons |
| 2024 | When are dynamical systems learned from time series data statistically accurate? Jeongjin Park, Nicole Yang, Nisha Chandramoorthy |
| 2024 | When does perceptual alignment benefit vision representations? Shobhita Sundaram, Stephanie Fu, Lukas Muttenthaler, Netanel Tamir, Lucy Chai, Simon Kornblith, Trevor Darrell, Phillip Isola |
| 2024 | When is Multicalibration Post-Processing Necessary? Dutch Hansen, Siddartha Devic, Preetum Nakkiran, Vatsal Sharan |
| 2024 | When is an Embedding Model More Promising than Another? Maxime Darrin, Philippe Formont, Ismail Ben Ayed, Jackie CK Cheung, Pablo Piantanida |
| 2024 | When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding. Marah Ghoummaid, Uri Shalit |
| 2024 | When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL. Lenart Treven, Bhavya Sukhija, Yarden As, Florian Dörfler, Andreas Krause |
| 2024 | Where Do Large Learning Rates Lead Us? Ildus Sadrtdinov, Maxim Kodryan, Eduard Pokonechny, Ekaterina Lobacheva, Dmitry P. Vetrov |
| 2024 | Where does In-context Learning Happen in Large Language Models? Suzanna Sia, David Mueller, Kevin Duh |
| 2024 | Where's Waldo: Diffusion Features For Personalized Segmentation and Retrieval. Dvir Samuel, Rami Ben-Ari, Matan Levy, Nir Darshan, Gal Chechik |
| 2024 | Who Evaluates the Evaluations? Objectively Scoring Text-to-Image Prompt Coherence Metrics with T2IScoreScore (TS2). Michael Saxon, Fatima Jahara, Mahsa Khoshnoodi, Yujie Lu, Aditya Sharma, William Yang Wang |
| 2024 | Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation. Trenton Chang, Lindsay A. Warrenburg, Sae-Hwan Park, Ravi B. Parikh, Maggie Makar, Jenna Wiens |
| 2024 | Who's asking? User personas and the mechanics of latent misalignment. Asma Ghandeharioun, Ann Yuan, Marius Guerard, Emily Reif, Michael A. Lepori, Lucas Dixon |
| 2024 | WhodunitBench: Evaluating Large Multimodal Agents via Murder Mystery Games. Junlin Xie, Ruifei Zhang, Zhihong Chen, Xiang Wan, Guanbin Li |
| 2024 | Why Do We Need Weight Decay in Modern Deep Learning? Francesco D'Angelo, Maksym Andriushchenko, Aditya Vardhan Varre, Nicolas Flammarion |
| 2024 | Why Go Full? Elevating Federated Learning Through Partial Network Updates. Haolin Wang, Xuefeng Liu, Jianwei Niu, Wenkai Guo, Shaojie Tang |
| 2024 | Why Transformers Need Adam: A Hessian Perspective. Yushun Zhang, Congliang Chen, Tian Ding, Ziniu Li, Ruoyu Sun, Zhi-Quan Luo |
| 2024 | Why Warmup the Learning Rate? Underlying Mechanisms and Improvements. Dayal Singh Kalra, Maissam Barkeshli |
| 2024 | Why are Visually-Grounded Language Models Bad at Image Classification? Yuhui Zhang, Alyssa Unell, Xiaohan Wang, Dhruba Ghosh, Yuchang Su, Ludwig Schmidt, Serena Yeung |
| 2024 | Why the Metric Backbone Preserves Community Structure. Maximilien Dreveton, Charbel Chucri, Matthias Grossglauser, Patrick Thiran |
| 2024 | Wide Two-Layer Networks can Learn from Adversarial Perturbations. Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki |
| 2024 | WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia. Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran T. Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi, Prasanna Sattigeri |
| 2024 | WikiDBs: A Large-Scale Corpus Of Relational Databases From Wikidata. Liane Vogel, Jan-Micha Bodensohn, Carsten Binnig |
| 2024 | WikiDO: A New Benchmark Evaluating Cross-Modal Retrieval for Vision-Language Models. Tankala Pavan Kalyan, Piyush Singh Pasi, Sahil Dharod, Azeem Motiwala, Preethi Jyothi, Aditi Chaudhary, Krishna Srinivasan |
| 2024 | Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections. Jiacong Xu, Yiqun Mei, Vishal M. Patel |
| 2024 | WildGaussians: 3D Gaussian Splatting In the Wild. Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Marc Pollefeys, Torsten Sattler |
| 2024 | WildGuard: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs. Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, Nouha Dziri |
| 2024 | WildPPG: A Real-World PPG Dataset of Long Continuous Recordings. Manuel Meier, Berken Utku Demirel, Christian Holz |
| 2024 | WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models. Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Yejin Choi, Nouha Dziri |
| 2024 | WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences. Yujie Lu, Dongfu Jiang, Wenhu Chen, William Yang Wang, Yejin Choi, Bill Yuchen Lin |
| 2024 | WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics. Neil Ashton, Jordan B. Angel, Aditya S. Ghate, Gaetan K. W. Kenway, Man Long Wong, Cetin C. Kiris, Astrid Walle, Danielle C. Maddix, Gary Page |
| 2024 | Wings: Learning Multimodal LLMs without Text-only Forgetting. Yi-Kai Zhang, Shiyin Lu, Yang Li, Yanqing Ma, Qingguo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye |
| 2024 | WizardArena: Post-training Large Language Models via Simulated Offline Chatbot Arena. Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Qingwei Lin, Jian-Guang Lou, Shifeng Chen, Yansong Tang, Weizhu Chen |
| 2024 | WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks. Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault Le Sellier de Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, Alexandre Drouin |
| 2024 | WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment. Hao Tang, Darren Key, Kevin Ellis |
| 2024 | Wormhole Loss for Partial Shape Matching. Amit Bracha, Thomas Dagès, Ron Kimmel |
| 2024 | Worst-Case Offline Reinforcement Learning with Arbitrary Data Support. Kohei Miyaguchi |
| 2024 | Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads. Avelina Asada Hadji-Kyriacou, Ognjen Arandjelovic |
| 2024 | X-Ray: A Sequential 3D Representation For Generation. Tao Hu, Wenhang Ge, Yuyang Zhao, Gim Hee Lee |
| 2024 | XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX. Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Artem Agarkov, Viacheslav Sinii, Sergey Kolesnikov |
| 2024 | XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation. Ziyi Wang, Yanbo Wang, Xumin Yu, Jie Zhou, Jiwen Lu |
| 2024 | YOLOv10: Real-Time End-to-End Object Detection. Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, Guiguang Ding |
| 2024 | Yo'LLaVA: Your Personalized Language and Vision Assistant. Thao Nguyen, Haotian Liu, Yuheng Li, Mu Cai, Utkarsh Ojha, Yong Jae Lee |
| 2024 | You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning. Théo Moutakanni, Maxime Oquab, Marc Szafraniec, Maria Vakalopoulou, Piotr Bojanowski |
| 2024 | You Only Cache Once: Decoder-Decoder Architectures for Language Models. Yutao Sun, Li Dong, Yi Zhu, Shaohan Huang, Wenhui Wang, Shuming Ma, Quanlu Zhang, Jianyong Wang, Furu Wei |
| 2024 | You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection. Mingbo Hong, Shen Cheng, Haibin Huang, Haoqiang Fan, Shuaicheng Liu |
| 2024 | YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals. Sandeep Mishra, Oindrila Saha, Alan C. Bovik |
| 2024 | Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training. Yunshu Wu, Yingtao Luo, Xianghao Kong, Vagelis Papalexakis, Greg Ver Steeg |
| 2024 | Your contrastive learning problem is secretly a distribution alignment problem. Zihao Chen, Chi-Heng Lin, Ran Liu, Jingyun Xiao, Eva L. Dyer |
| 2024 | ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving. Tao Ma, Hongbin Zhou, Qiusheng Huang, Xuemeng Yang, Jianfei Guo, Bo Zhang, Min Dou, Yu Qiao, Botian Shi, Hongsheng Li |
| 2024 | ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination. Xihuai Wang, Shao Zhang, Wenhao Zhang, Wentao Dong, Jingxiao Chen, Ying Wen, Weinan Zhang |
| 2024 | Zero-Shot Event-Intensity Asymmetric Stereo via Visual Prompting from Image Domain. Hanyue Lou, Jinxiu (Sherry) Liang, Minggui Teng, Bin Fan, Yong Xu, Boxin Shi |
| 2024 | Zero-Shot Reinforcement Learning from Low Quality Data. Scott R. Jeen, Tom Bewley, Jonathan M. Cullen |
| 2024 | Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assembly. Junsheng Zhou, Yu-Shen Liu, Zhizhong Han |
| 2024 | Zero-Shot Tokenizer Transfer. Benjamin Minixhofer, Edoardo Maria Ponti, Ivan Vulic |
| 2024 | Zero-Shot Transfer of Neural ODEs. Tyler Ingebrand, Adam J. Thorpe, Ufuk Topcu |
| 2024 | Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection. Jieren Deng, Haojian Zhang, Kun Ding, Jianhua Hu, Xingxuan Zhang, Yunkuan Wang |
| 2024 | Zero-shot Image Editing with Reference Imitation. Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, Hengshuang Zhao |
| 2024 | Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering. Ido Sobol, Chenfeng Xu, Or Litany |
| 2024 | ZeroMark: Towards Dataset Ownership Verification without Disclosing Watermark. Junfeng Guo, Yiming Li, Ruibo Chen, Yihan Wu, Chenxi Liu, Heng Huang |
| 2024 | Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion. Ye He, Kevin Rojas, Molei Tao |
| 2024 | ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification. Yefei He, Luoming Zhang, Weijia Wu, Jing Liu, Hong Zhou, Bohan Zhuang |
| 2024 | Zipfian Whitening. Sho Yokoi, Han Bao, Hiroto Kurita, Hidetoshi Shimodaira |
| 2024 | Zipper: Addressing Degeneracy in Algorithm-Agnostic Inference. Geng Chen, Yinxu Jia, Guanghui Wang, Changliang Zou |
| 2024 | bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction. Yehe Liu, Alexander Krull, Hector Basevi, Ales Leonardis, Michael W. Jenkins |
| 2024 | cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers. Anirudh Sundar, Jin Xu, William Gay, Christopher Richardson, Larry Heck |
| 2024 | dattri: A Library for Efficient Data Attribution. Junwei Deng, Ting-Wei Li, Shiyuan Zhang, Shixuan Liu, Yijun Pan, Hao Huang, Xinhe Wang, Pingbang Hu, Xingjian Zhang, Jiaqi W. Ma |
| 2024 | dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans. Marek Herde, Denis Huseljic, Lukas Rauch, Bernhard Sick |
| 2024 | e-COP : Episodic Constrained Optimization of Policies. Akhil Agnihotri, Rahul Jain, Deepak Ramachandran, Sahil Singla |
| 2024 | eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling. Matthew Dowling, Yuan Zhao, Memming Park |
| 2024 | einspace: Searching for Neural Architectures from Fundamental Operations. Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley |
| 2024 | emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation. Sasha Salter, Richard Warren, Collin Schlager, Adrian Spurr, Shangchen Han, Rohin Bhasin, Yujun Cai, Peter Walkington, Anuoluwapo Bolarinwa, Robert Wang, Nathan Danielson, Josh Merel, Eftychios A. Pnevmatikakis, Jesse Marshall |
| 2024 | emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography. Viswanath Sivakumar, Jeffrey Seely, Alan Du, Sean R. Bittner, Adam Berenzweig, Anuoluwapo Bolarinwa, Alexandre Gramfort, Michael I. Mandel |
| 2024 | fMRI predictors based on language models of increasing complexity recover brain left lateralization. Laurent Bonnasse-Gahot, Christophe Pallier |
| 2024 | iVideoGPT: Interactive VideoGPTs are Scalable World Models. Jialong Wu, Shaofeng Yin, Ningya Feng, Xu He, Dong Li, Jianye Hao, Mingsheng Long |
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| 2024 | pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization. Matthew C. Bendel, Rizwan Ahmad, Philip Schniter |
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| 2024 | β-DPO: Direct Preference Optimization with Dynamic β. Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin Ding, Xiang Wang, Xiangnan He |
| 2024 | μP Moritz Haas, Jin Xu, Volkan Cevher, Leena Chennuru Vankadara |