ICML A*

2611 papers

YearTitle / Authors
20243D Geometric Shape Assembly via Efficient Point Cloud Matching.
Nahyuk Lee, Juhong Min, Junha Lee, Seungwook Kim, Kanghee Lee, Jaesik Park, Minsu Cho
20243D-VLA: A 3D Vision-Language-Action Generative World Model.
Haoyu Zhen, Xiaowen Qiu, Peihao Chen, Jincheng Yang, Xin Yan, Yilun Du, Yining Hong, Chuang Gan
2024A Bayesian Approach to Online Planning.
Nir Greshler, David Ben-Eli, Carmel Rabinovitz, Gabi Guetta, Liran Gispan, Guy Zohar, Aviv Tamar
2024A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models.
Sebastian Gregor Gruber, Florian Buettner
2024A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design.
Zhihai Wang, Lei Chen, Jie Wang, Yinqi Bai, Xing Li, Xijun Li, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu
2024A Closer Look at the Limitations of Instruction Tuning.
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar, Ramaneswaran S., Deepali Aneja, Zeyu Jin, Ramani Duraiswami, Dinesh Manocha
2024A Computational Framework for Solving Wasserstein Lagrangian Flows.
Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani
2024A Contextual Combinatorial Bandit Approach to Negotiation.
Yexin Li, Zhancun Mu, Siyuan Qi
2024A Dense Reward View on Aligning Text-to-Image Diffusion with Preference.
Shentao Yang, Tianqi Chen, Mingyuan Zhou
2024A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing.
Chengrui Li, Weihan Li, Yule Wang, Anqi Wu
2024A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization.
Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner
2024A Distributional Analogue to the Successor Representation.
Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization.
Hongchang Gao
2024A Dual-module Framework for Counterfactual Estimation over Time.
Xin Wang, Shengfei Lyu, Lishan Yang, Yibing Zhan, Huanhuan Chen
2024A Dynamic Algorithm for Weighted Submodular Cover Problem.
Kiarash Banihashem, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh
2024A Dynamical Model of Neural Scaling Laws.
Blake Bordelon, Alexander B. Atanasov, Cengiz Pehlevan
2024A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization.
Xinwen Zhang, Ali Payani, Myungjin Lee, Richard Souvenir, Hongchang Gao
2024A Field Guide for Pacing Budget and ROS Constraints.
Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
2024A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models.
Jiayi Wang, Zhengling Qi, Raymond K. W. Wong
2024A Fixed-Point Approach for Causal Generative Modeling.
Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
2024A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks.
Nicholas Monath, Will Sussman Grathwohl, Michael Boratko, Rob Fergus, Andrew McCallum, Manzil Zaheer
2024A General Framework for Learning from Weak Supervision.
Hao Chen, Jindong Wang, Lei Feng, Xiang Li, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj
2024A General Framework for Sequential Decision-Making under Adaptivity Constraints.
Nuoya Xiong, Zhaoran Wang, Zhuoran Yang
2024A General Online Algorithm for Optimizing Complex Performance Metrics.
Wojciech Kotlowski, Marek Wydmuch, Erik Schultheis, Rohit Babbar, Krzysztof Dembczynski
2024A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts.
Huy Nguyen, Pedram Akbarian, TrungTin Nguyen, Nhat Ho
2024A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective.
Baohong Li, Haoxuan Li, Anpeng Wu, Minqin Zhu, Shiyuan Peng, Qingyu Cao, Kun Kuang
2024A Geometric Decomposition of Finite Games: Convergence vs. Recurrence under Exponential Weights.
Davide Legacci, Panayotis Mertikopoulos, Bary S. R. Pradelski
2024A Geometric Explanation of the Likelihood OOD Detection Paradox.
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
2024A Global Geometric Analysis of Maximal Coding Rate Reduction.
Peng Wang, Huikang Liu, Druv Pai, Yaodong Yu, Zhihui Zhu, Qing Qu, Yi Ma
2024A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer.
Zhangyang Gao, Daize Dong, Cheng Tan, Jun Xia, Bozhen Hu, Stan Z. Li
2024A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design.
Zhihai Wang, Jie Wang, Dongsheng Zuo, Yunjie Ji, Xilin Xia, Yuzhe Ma, Jianye Hao, Mingxuan Yuan, Yongdong Zhang, Feng Wu
2024A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts.
Kuang-Huei Lee, Xinyun Chen, Hiroki Furuta, John F. Canny, Ian Fischer
2024A Language Model's Guide Through Latent Space.
Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, Thomas Hofmann
2024A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM).
Dehao Yuan, Cornelia Fermüller, Tahseen Rabbani, Furong Huang, Yiannis Aloimonos
2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity.
Andrew Lee, Xiaoyan Bai, Itamar Pres, Martin Wattenberg, Jonathan K. Kummerfeld, Rada Mihalcea
2024A Minimaximalist Approach to Reinforcement Learning from Human Feedback.
Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal
2024A Multimodal Automated Interpretability Agent.
Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Achyuta Rajaram, Evan Hernandez, Jacob Andreas, Antonio Torralba
2024A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering.
Vincent Cohen-Addad, Tommaso d'Orsi, Aida Mousavifar
2024A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness.
Xiaochuan Gong, Jie Hao, Mingrui Liu
2024A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data.
Wenqiang Li, Weijun Li, Lina Yu, Min Wu, Linjun Sun, Jingyi Liu, Yanjie Li, Shu Wei, Yusong Deng, Meilan Hao
2024A Neural-Preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions.
Kai Weixian Lan, Elias Gueidon, Ayano Kaneda, Julian Panetta, Joseph Teran
2024A New Branch-and-Bound Pruning Framework for ℓ0-Regularized Problems.
Théo Guyard, Cédric Herzet, Clément Elvira, Ayse-Nur Arslan
2024A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces.
Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado, Alessandra Pascale, Jonathan P. Epperlein
2024A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization.
Ashwinee Panda, Xinyu Tang, Saeed Mahloujifar, Vikash Sehwag, Prateek Mittal
2024A New Robust Partial p-Wasserstein-Based Metric for Comparing Distributions.
Sharath Raghvendra, Pouyan Shirzadian, Kaiyi Zhang
2024A New Theoretical Perspective on Data Heterogeneity in Federated Optimization.
Jiayi Wang, Shiqiang Wang, Rong-Rong Chen, Mingyue Ji
2024A Persuasive Approach to Combating Misinformation.
Safwan Hossain, Andjela Mladenovic, Yiling Chen, Gauthier Gidel
2024A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs.
Kihyuk Hong, Ambuj Tewari
2024A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs.
Lars Veefkind, Gabriele Cesa
2024A Provable Decision Rule for Out-of-Distribution Detection.
Xinsong Ma, Xin Zou, Weiwei Liu
2024A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts.
Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen, Christopher D. Carothers
2024A Rate-Distortion View of Uncertainty Quantification.
Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen, Artur Dubrawski
2024A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models.
Yihan Wu, Zhengmian Hu, Junfeng Guo, Hongyang Zhang, Heng Huang
2024A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models.
Tae Hong Moon, Moonseok Choi, EungGu Yun, Jongmin Yoon, Gayoung Lee, Jaewoong Cho, Juho Lee
2024A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes.
Zhenwei Lin, Chenyu Xue, Qi Deng, Yinyu Ye
2024A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss
2024A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction.
Keqiang Yan, Alexandra Saxton, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
2024A Sparsity Principle for Partially Observable Causal Representation Learning.
Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
2024A Statistical Framework for Data-dependent Retrieval-Augmented Models.
Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
2024A Statistical Theory of Regularization-Based Continual Learning.
Xuyang Zhao, Huiyuan Wang, Weiran Huang, Wei Lin
2024A Study of First-Order Methods with a Deterministic Relative-Error Gradient Oracle.
Nadav Hallak, Kfir Yehuda Levy
2024A Subquadratic Time Algorithm for Robust Sparse Mean Estimation.
Ankit Pensia
2024A Tale of Tails: Model Collapse as a Change of Scaling Laws.
Elvis Dohmatob, Yunzhen Feng, Pu Yang, François Charton, Julia Kempe
2024A Tensor Decomposition Perspective on Second-order RNNs.
Maude Lizaire, Michael Rizvi-Martel, Marawan Gamal Abdel Hameed, Guillaume Rabusseau
2024A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks.
Boqi Li, Weiwei Liu
2024A Theory of Fault-Tolerant Learning.
Changlong Wu, Yifan Wang, Ananth Grama
2024A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks.
Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban
2024A Touch, Vision, and Language Dataset for Multimodal Alignment.
Letian Fu, Gaurav Datta, Huang Huang, William Chung-Ho Panitch, Jaimyn Drake, Joseph Ortiz, Mustafa Mukadam, Mike Lambeta, Roberto Calandra, Ken Goldberg
2024A Unified Adaptive Testing System Enabled by Hierarchical Structure Search.
Junhao Yu, Yan Zhuang, Zhenya Huang, Qi Liu, Xin Li, Rui Li, Enhong Chen
2024A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples.
Ben Adcock, Juan M. Cardenas, Nick C. Dexter
2024A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback.
Kihyun Kim, Jiawei Zhang, Asuman E. Ozdaglar, Pablo A. Parrilo
2024A Unified View of FANOVA: A Comprehensive Bayesian Framework for Component Selection and Estimation.
Yosra Marnissi, Maxime Leiber
2024A Universal Class of Sharpness-Aware Minimization Algorithms.
Behrooz Tahmasebi, Ashkan Soleymani, Dara Bahri, Stefanie Jegelka, Patrick Jaillet
2024A Universal Transfer Theorem for Convex Optimization Algorithms Using Inexact First-order Oracles.
Phillip A. Kerger, Marco Molinaro, Hongyi Jiang, Amitabh Basu
2024A connection between Tempering and Entropic Mirror Descent.
Nicolas Chopin, Francesca R. Crucinio, Anna Korba
2024A decoder-only foundation model for time-series forecasting.
Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou
2024A fast algorithm to simulate nonlinear resistive networks.
Benjamin Scellier
2024A sampling theory perspective on activations for implicit neural representations.
Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Alexander Long, Simon Lucey
2024A2Q+: Improving Accumulator-Aware Weight Quantization.
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig, Yaman Umuroglu
2024A3S: A General Active Clustering Method with Pairwise Constraints.
Xun Deng, Junlong Liu, Han Zhong, Fuli Feng, Chen Shen, Xiangnan He, Jieping Ye, Zheng Wang
2024ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization.
Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu
2024ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation.
Ziao Guo, Yang Li, Chang Liu, Wenli Ouyang, Junchi Yan
2024ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints.
Akhil Agnihotri, Rahul Jain, Haipeng Luo
2024AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors.
Yucen Wang, Shenghua Wan, Le Gan, Shuai Feng, De-Chuan Zhan
2024AI Alignment with Changing and Influenceable Reward Functions.
Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell, Anca D. Dragan
2024AI Control: Improving Safety Despite Intentional Subversion.
Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan, Fabien Roger
2024ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data.
Carmen Martin-Turrero, Maxence Bouvier, Manuel Breitenstein, Pietro Zanuttigh, Vincent Parret
2024AMPA: Adaptive Mixed Precision Allocation for Low-Bit Integer Training.
Li Ding, Wen Fei, Yuyang Huang, Shuangrui Ding, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
2024AND: Audio Network Dissection for Interpreting Deep Acoustic Models.
Tung-Yu Wu, Yu-Xiang Lin, Tsui-Wei Weng
2024APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference.
Bowen Zhao, Hannaneh Hajishirzi, Qingqing Cao
2024AST-T5: Structure-Aware Pretraining for Code Generation and Understanding.
Linyuan Gong, Mostafa Elhoushi, Alvin Cheung
2024ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories.
Qianlan Yang, Yu-Xiong Wang
2024Absolute Policy Optimization: Enhancing Lower Probability Bound of Performance with High Confidence.
Weiye Zhao, Feihan Li, Yifan Sun, Rui Chen, Tianhao Wei, Changliu Liu
2024Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion.
Yang Cai, Argyris Oikonomou, Weiqiang Zheng
2024Accelerated Policy Gradient for s-rectangular Robust MDPs with Large State Spaces.
Ziyi Chen, Heng Huang
2024Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning.
Yen-Ju Chen, Nai-Chieh Huang, Ching-pei Lee, Ping-Chun Hsieh
2024Accelerated Speculative Sampling Based on Tree Monte Carlo.
Zhengmian Hu, Heng Huang
2024Accelerating Convergence in Bayesian Few-Shot Classification.
Tianjun Ke, Haoqun Cao, Feng Zhou
2024Accelerating Convergence of Score-Based Diffusion Models, Provably.
Gen Li, Yu Huang, Timofey Efimov, Yuting Wei, Yuejie Chi, Yuxin Chen
2024Accelerating Federated Learning with Quick Distributed Mean Estimation.
Ran Ben-Basat, Shay Vargaftik, Amit Portnoy, Gil Einziger, Yaniv Ben-Itzhak, Michael Mitzenmacher
2024Accelerating Heterogeneous Federated Learning with Closed-form Classifiers.
Eros Fanì, Raffaello Camoriano, Barbara Caputo, Marco Ciccone
2024Accelerating Iterative Retrieval-augmented Language Model Serving with Speculation.
Zhihao Zhang, Alan Zhu, Lijie Yang, Yihua Xu, Lanting Li, Phitchaya Mangpo Phothilimthana, Zhihao Jia
2024Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving.
Sohei Arisaka, Qianxiao Li
2024Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need.
Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin T. Carlberg, Neil Walton, Kody J. H. Law
2024Accelerating PDE Data Generation via Differential Operator Action in Solution Space.
Huanshuo Dong, Hong Wang, Haoyang Liu, Jian Luo, Jie Wang
2024Accelerating Parallel Sampling of Diffusion Models.
Zhiwei Tang, Jiasheng Tang, Hao Luo, Fan Wang, Tsung-Hui Chang
2024Accelerating Transformer Pre-training with 2: 4 Sparsity.
Yuezhou Hu, Kang Zhao, Weiyu Huang, Jianfei Chen, Jun Zhu
2024Accurate LoRA-Finetuning Quantization of LLMs via Information Retention.
Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang, Shouda Liu, Jie Luo, Xianglong Liu, Michele Magno
2024Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning.
Do-Yeon Kim, Dong-Jun Han, Jun Seo, Jaekyun Moon
2024Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling.
Mingze Wang, Zeping Min, Lei Wu
2024Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts.
Onur Celik, Aleksandar Taranovic, Gerhard Neumann
2024Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition.
Michael Valancius, Max Lennon, Junier Oliva
2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations.
Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Jiayuan He, Yinghai Lu, Yu Shi
2024Activation-Descent Regularization for Input Optimization of ReLU Networks.
Hongzhan Yu, Sicun Gao
2024Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choice.
Masahiro Kato, Akihiro Oga, Wataru Komatsubara, Ryo Inokuchi
2024Active Label Correction for Semantic Segmentation with Foundation Models.
Hoyoung Kim, Sehyun Hwang, Suha Kwak, Jungseul Ok
2024Active Preference Learning for Large Language Models.
William Muldrew, Peter Hayes, Mingtian Zhang, David Barber
2024Active Ranking and Matchmaking, with Perfect Matchings.
Hafedh El Ferchichi, Matthieu Lerasle, Vianney Perchet
2024Active Statistical Inference.
Tijana Zrnic, Emmanuel J. Candès
2024Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models.
Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi
2024Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control.
Dongyoon Hwang, Byungkun Lee, Hojoon Lee, Hyunseung Kim, Jaegul Choo
2024Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Rate.
Yuancheng Xu, Chenghao Deng, Yanchao Sun, Ruijie Zheng, Xiyao Wang, Jieyu Zhao, Furong Huang
2024Adaptive Accompaniment with ReaLchords.
Yusong Wu, Tim Cooijmans, Kyle Kastner, Adam Roberts, Ian Simon, Alexander Scarlatos, Chris Donahue, Cassie Tarakajian, Shayegan Omidshafiei, Aaron C. Courville, Pablo Samuel Castro, Natasha Jaques, Cheng-Zhi Anna Huang
2024Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning.
Tenglong Liu, Yang Li, Yixing Lan, Hao Gao, Wei Pan, Xin Xu
2024Adaptive Conformal Inference by Betting.
Aleksandr Podkopaev, Dong Xu, Kuang-Chih Lee
2024Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity.
Xudong Li, Timin Gao, Runze Hu, Yan Zhang, Shengchuan Zhang, Xiawu Zheng, Jingyuan Zheng, Yunhang Shen, Ke Li, Yutao Liu, Pingyang Dai, Rongrong Ji
2024Adaptive Group Personalization for Federated Mutual Transfer Learning.
Haoqing Xu, Dian Shen, Meng Wang, Beilun Wang
2024Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing.
Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz
2024Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation.
Ignat Georgiev, Krishnan Srinivasan, Jie Xu, Eric Heiden, Animesh Garg
2024Adaptive Observation Cost Control for Variational Quantum Eigensolvers.
Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima
2024Adaptive Online Experimental Design for Causal Discovery.
Muhammad Qasim Elahi, Lai Wei, Murat Kocaoglu, Mahsa Ghasemi
2024Adaptive Proximal Gradient Methods Are Universal Without Approximation.
Konstantinos A. Oikonomidis, Emanuel Laude, Puya Latafat, Andreas Themelis, Panagiotis Patrinos
2024Adaptive Robust Learning using Latent Bernoulli Variables.
Aleksandr Karakulev, Dave Zachariah, Prashant Singh
2024Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction.
Chen-Yu Yen, Raghav Singhal, Umang Sharma, Rajesh Ranganath, Sumit Chopra, Lerrel Pinto
2024Adaptive Stabilization Based on Machine Learning for Column Generation.
Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Andrew C. Eberhard, Guangquan Zhang
2024Adaptive Text Watermark for Large Language Models.
Yepeng Liu, Yuheng Bu
2024Adaptive-Gradient Policy Optimization: Enhancing Policy Learning in Non-Smooth Differentiable Simulations.
Feng Gao, Liangzhi Shi, Shenao Zhang, Zhaoran Wang, Yi Wu
2024Adaptively Learning to Select-Rank in Online Platforms.
Jingyuan Wang, Perry Dong, Ying Jin, Ruohan Zhan, Zhengyuan Zhou
2024Adaptively Perturbed Mirror Descent for Learning in Games.
Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki
2024AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion.
Adeesh Kolluru, John R. Kitchin
2024Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment.
Chen Zhang, Qiang He, Yuan Zhou, Elvis S. Liu, Hong Wang, Jian Zhao, Yang Wang
2024Advancing Dynamic Sparse Training by Exploring Optimization Opportunities.
Jie Ji, Gen Li, Lu Yin, Minghai Qin, Geng Yuan, Linke Guo, Shiwei Liu, Xiaolong Ma
2024Adversarial Attacks on Combinatorial Multi-Armed Bandits.
Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu, Haoyu Zhao
2024Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies.
Brian R. Bartoldson, James Diffenderfer, Konstantinos Parasyris, Bhavya Kailkhura
2024Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework.
Haonan Huang, Guoxu Zhou, Yanghang Zheng, Yuning Qiu, Andong Wang, Qibin Zhao
2024Adversarially Robust Hypothesis Transfer Learning.
Yunjuan Wang, Raman Arora
2024AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning.
Dong Chen, Hongyuan Qu, Guangwu Xu
2024Agent Instructs Large Language Models to be General Zero-Shot Reasoners.
Nicholas Crispino, Kyle Montgomery, Fankun Zeng, Dawn Song, Chenguang Wang
2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast.
Xiangming Gu, Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Ye Wang, Jing Jiang, Min Lin
2024Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs.
Stelios Triantafyllou, Aleksa Sukovic, Debmalya Mandal, Goran Radanovic
2024Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms.
Yichen Li, Chicheng Zhang
2024Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms.
Avishek Ghosh, Arya Mazumdar
2024Agnostic Sample Compression Schemes for Regression.
Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
2024Ai-sampler: Adversarial Learning of Markov kernels with involutive maps.
Evgenii Egorov, Riccardo Valperga, Stratis Gavves
2024Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models.
Jan van den Brand, Zhao Song, Tianyi Zhou
2024Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models.
Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin
2024Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses.
Shaojie Li, Bowei Zhu, Yong Liu
2024Align Your Steps: Optimizing Sampling Schedules in Diffusion Models.
Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
2024Aligned Objective for Soft-Pseudo-Label Generation in Supervised Learning.
Ning Xu, Yihao Hu, Congyu Qiao, Xin Geng
2024Aligning Transformers with Weisfeiler-Leman.
Luis Müller, Christopher Morris
2024All-in-one simulation-based inference.
Manuel Glöckler, Michael Deistler, Christian Dietrich Weilbach, Frank Wood, Jakob H. Macke
2024Allocation Requires Prediction Only if Inequality Is Low.
Ali Shirali, Rediet Abebe, Moritz Hardt
2024AlphaFold Meets Flow Matching for Generating Protein Ensembles.
Bowen Jing, Bonnie Berger, Tommi S. Jaakkola
2024AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training.
Ziyu Wan, Xidong Feng, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, Jun Wang
2024Ambiguity-Aware Abductive Learning.
Hao-Yuan He, Hui Sun, Zheng Xie, Ming Li
2024Ameliorate Spurious Correlations in Dataset Condensation.
Justin Cui, Ruochen Wang, Yuanhao Xiong, Cho-Jui Hsieh
2024Amend to Alignment: Decoupled Prompt Tuning for Mitigating Spurious Correlation in Vision-Language Models.
Jie Zhang, Xiaosong Ma, Song Guo, Peng Li, Wenchao Xu, Xueyang Tang, Zicong Hong
2024Amortized Equation Discovery in Hybrid Dynamical Systems.
Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Stratis Gavves
2024Amortized Variational Deep Kernel Learning.
Alan L. S. Matias, César Lincoln C. Mattos, João Paulo Pordeus Gomes, Diego Mesquita
2024Amortizing Pragmatic Program Synthesis with Rankings.
Yewen Pu, Saujas Vaduguru, Priyan Vaithilingam, Elena L. Glassman, Daniel Fried
2024An Analysis of Linear Time Series Forecasting Models.
William Toner, Luke Nicholas Darlow
2024An Effective Dynamic Gradient Calibration Method for Continual Learning.
Weichen Lin, Jiaxiang Chen, Ruomin Huang, Hu Ding
2024An Efficient Maximal Ancestral Graph Listing Algorithm.
Tian-Zuo Wang, Wen-Bo Du, Zhi-Hua Zhou
2024An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems.
Hitesh Tulsiani, David M. Chan, Shalini Ghosh, Garima Lalwani, Prabhat Pandey, Ankish Bansal, Sri Garimella, Ariya Rastrow, Björn Hoffmeister
2024An Embodied Generalist Agent in 3D World.
Jiangyong Huang, Silong Yong, Xiaojian Ma, Xiongkun Linghu, Puhao Li, Yan Wang, Qing Li, Song-Chun Zhu, Baoxiong Jia, Siyuan Huang
2024An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series.
Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu
2024An Empirical Study Into What Matters for Calibrating Vision-Language Models.
Weijie Tu, Weijian Deng, Dylan Campbell, Stephen Gould, Tom Gedeon
2024An Empirical Study of Realized GNN Expressiveness.
Yanbo Wang, Muhan Zhang
2024An Explicit Frame Construction for Normalizing 3D Point Clouds.
Justin M. Baker, Shih-Hsin Wang, Tommaso de Fernex, Bao Wang
2024An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning.
Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare
2024An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks.
Zhifa Ke, Zaiwen Wen, Junyu Zhang
2024An Independence-promoting Loss for Music Generation with Language Models.
Jean-Marie Lemercier, Simon Rouard, Jade Copet, Yossi Adi, Alexandre Défossez
2024An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network.
TaeYoung Kim, Hongseok Yang
2024An Information Theoretic Approach to Interaction-Grounded Learning.
Xiaoyan Hu, Farzan Farnia, Ho-fung Leung
2024An Information-Theoretic Analysis of In-Context Learning.
Hong Jun Jeon, Jason D. Lee, Qi Lei, Benjamin Van Roy
2024An Interpretable Evaluation of Entropy-based Novelty of Generative Models.
Jingwei Zhang, Cheuk Ting Li, Farzan Farnia
2024An Intrinsic Vector Heat Network.
Alexander Gao, Maurice Chu, Mubbasir Kapadia, Ming C. Lin, Hsueh-Ti Derek Liu
2024An Iterative Min-Min Optimization Method for Sparse Bayesian Learning.
Yasen Wang, Junlin Li, Zuogong Yue, Ye Yuan
2024An LLM Compiler for Parallel Function Calling.
Sehoon Kim, Suhong Moon, Ryan Tabrizi, Nicholas Lee, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
2024An Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization.
Emre Sahinoglu, Shahin Shahrampour
2024An Unsupervised Approach for Periodic Source Detection in Time Series.
Berken Utku Demirel, Christian Holz
2024An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation.
Jonas Arruda, Yannik Schälte, Clemens Peiter, Olga Teplytska, Ulrich Jaehde, Jan Hasenauer
2024Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts.
Xiaowen Yang, Wenda Wei, Jie-Jing Shao, Yufeng Li, Zhi-Hua Zhou
2024Analyzing Dα seeding for k-means.
Étienne Bamas, Sai Ganesh Nagarajan, Ola Svensson
2024Antibody Design Using a Score-based Diffusion Model Guided by Evolutionary, Physical and Geometric Constraints.
Tian Zhu, Milong Ren, Haicang Zhang
2024Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs.
Yeonhong Park, Jake Hyun, SangLyul Cho, Bonggeun Sim, Jae W. Lee
2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls.
Yu Du, Fangyun Wei, Hongyang Zhang
2024Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formula.
Kirill Brilliantov, Fedor Pavutnitskiy, Dmitry Pasechnyuk, German Magai
2024Approximate Nearest Neighbor Search with Window Filters.
Joshua Engels, Benjamin Landrum, Shangdi Yu, Laxman Dhulipala, Julian Shun
2024AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA.
Weitao Feng, Wenbo Zhou, Jiyan He, Jie Zhang, Tianyi Wei, Guanlin Li, Tianwei Zhang, Weiming Zhang, Nenghai Yu
2024ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL.
Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, Aviral Kumar
2024Arrows of Time for Large Language Models.
Vassilis Papadopoulos, Jérémie Wenger, Clément Hongler
2024ArtWhisperer: A Dataset for Characterizing Human-AI Interactions in Artistic Creations.
Kailas Vodrahalli, James Zou
2024Assessing Large Language Models on Climate Information.
Jannis Bulian, Mike S. Schäfer, Afra Amini, Heidi Lam, Massimiliano Ciaramita, Ben Gaiarin, Michelle Chen Huebscher, Christian Buck, Niels Mede, Markus Leippold, Nadine Strauß
2024Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications.
Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, Peter Henderson
2024Asymmetry in Low-Rank Adapters of Foundation Models.
Jiacheng Zhu, Kristjan H. Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon
2024Asymptotically Optimal and Computationally Efficient Average Treatment Effect Estimation in A/B testing.
Vikas Deep, Achal Bassamboo, Sandeep K. Juneja
2024Asymptotics of Learning with Deep Structured (Random) Features.
Dominik Schröder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro
2024Asymptotics of feature learning in two-layer networks after one gradient-step.
Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro
2024AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios.
Zhongzhan Huang, Mingfu Liang, Shanshan Zhong, Liang Lin
2024Attack-free Evaluating and Enhancing Adversarial Robustness on Categorical Data.
Yujun Zhou, Yufei Han, Haomin Zhuang, Hongyan Bao, Xiangliang Zhang
2024Attention Meets Post-hoc Interpretability: A Mathematical Perspective.
Gianluigi Lopardo, Frédéric Precioso, Damien Garreau
2024AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers.
Reduan Achtibat, Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Aakriti Jain, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek
2024Attribute Based Interpretable Evaluation Metrics for Generative Models.
Dongkyun Kim, Mingi Kwon, Youngjung Uh
2024Auctionformer: A Unified Deep Learning Algorithm for Solving Equilibrium Strategies in Auction Games.
Kexin Huang, Ziqian Chen, Xue Wang, Chongming Gao, Jinyang Gao, Bolin Ding, Xiang Wang
2024Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities.
Zhifeng Kong, Arushi Goel, Rohan Badlani, Wei Ping, Rafael Valle, Bryan Catanzaro
2024Auditing Private Prediction.
Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher A. Choquette-Choo, Milad Nasr
2024Augmenting Decision with Hypothesis in Reinforcement Learning.
Nguyen Minh Quang, Hady W. Lauw
2024Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks.
Lihao Wang, Zhaofei Yu
2024Auto-Encoding Morph-Tokens for Multimodal LLM.
Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow, Shuicheng Yan, Tat-Seng Chua, Yueting Zhuang, Hanwang Zhang
2024Auto-Linear Phenomenon in Subsurface Imaging.
Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Youzuo Lin
2024Auto-Regressive Next-Token Predictors are Universal Learners.
Eran Malach
2024AutoOS: Make Your OS More Powerful by Exploiting Large Language Models.
Huilai Chen, Yuanbo Wen, Limin Cheng, Shouxu Kuang, Yumeng Liu, Weijia Li, Ling Li, Rui Zhang, Xinkai Song, Wei Li, Qi Guo, Yunji Chen
2024Autoencoding Conditional Neural Processes for Representation Learning.
Victor Prokhorov, Ivan Titov, N. Siddharth
2024Autoformalizing Euclidean Geometry.
Logan Murphy, Kaiyu Yang, Jialiang Sun, Zhaoyu Li, Anima Anandkumar, Xujie Si
2024Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation.
Gauthier Guinet, Behrooz Omidvar-Tehrani, Anoop Deoras, Laurent Callot
2024Automated Loss function Search for Class-imbalanced Node Classification.
Xinyu Guo, Kai Wu, Xiaoyu Zhang, Jing Liu
2024Automated Statistical Model Discovery with Language Models.
Michael Y. Li, Emily B. Fox, Noah D. Goodman
2024Automating the Selection of Proxy Variables of Unmeasured Confounders.
Feng Xie, Zhengming Chen, Shanshan Luo, Wang Miao, Ruichu Cai, Zhi Geng
2024Autonomous Sparse Mean-CVaR Portfolio Optimization.
Yizun Lin, Yangyu Zhang, Zhao-Rong Lai, Cheng Li
2024Averaging n-step Returns Reduces Variance in Reinforcement Learning.
Brett Daley, Martha White, Marlos C. Machado
2024BAGEL: Bootstrapping Agents by Guiding Exploration with Language.
Shikhar Murty, Christopher D. Manning, Peter Shaw, Mandar Joshi, Kenton Lee
2024BAT: Learning to Reason about Spatial Sounds with Large Language Models.
Zhisheng Zheng, Puyuan Peng, Ziyang Ma, Xie Chen, Eunsol Choi, David Harwath
2024BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models.
Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang, Bo Dai
2024BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation.
Daeun Lee, Jaehong Yoon, Sung Ju Hwang
2024BLO-SAM: Bi-level Optimization Based Finetuning of the Segment Anything Model for Overfitting-Preventing Semantic Segmentation.
Li Zhang, Youwei Liang, Ruiyi Zhang, Amirhosein Javadi, Pengtao Xie
2024BOtied: Multi-objective Bayesian optimization with tied multivariate ranks.
Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho
2024BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback.
Gaurav Pandey, Yatin Nandwani, Tahira Naseem, Mayank Mishra, Guangxuan Xu, Dinesh Raghu, Sachindra Joshi, Asim Munawar, Ramón Fernandez Astudillo
2024BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges.
Hoyong Choi, Nohyun Ki, Hye Won Chung
2024BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks.
Zhiyuan Cheng, Zhaoyi Liu, Tengda Guo, Shiwei Feng, Dongfang Liu, Mingjie Tang, Xiangyu Zhang
2024Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise.
Xi Chen, Zhewen Hou, Christopher A. Metzler, Arian Maleki, Shirin Jalali
2024Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance.
Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer
2024Balanced Resonate-and-Fire Neurons.
Saya Higuchi, Sebastian Kairat, Sander M. Bohté, Sebastian Otte
2024Balancing Feature Similarity and Label Variability for Optimal Size-Aware One-shot Subset Selection.
Abhinab Acharya, Dayou Yu, Qi Yu, Xumin Liu
2024Balancing Similarity and Complementarity for Federated Learning.
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang, Bo Han, Gang Niu, Masashi Sugiyama, Changshui Zhang
2024Barrier Algorithms for Constrained Non-Convex Optimization.
Pavel E. Dvurechensky, Mathias Staudigl
2024Batch Singular Value Polarization and Weighted Semantic Augmentation for Universal Domain Adaptation.
Wangzi Qi, Wei Wang, Chao Huang, Jie Wen, Cong Wang
2024Batch and match: black-box variational inference with a score-based divergence.
Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul
2024BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition.
Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun
2024Bayesian Adaptation of Network Depth and Width for Continual Learning.
Jeevan Thapa, Rui Li
2024Bayesian Design Principles for Offline-to-Online Reinforcement Learning.
Hao Hu, Yiqin Yang, Jianing Ye, Chengjie Wu, Ziqing Mai, Yujing Hu, Tangjie Lv, Changjie Fan, Qianchuan Zhao, Chongjie Zhang
2024Bayesian Exploration Networks.
Mattie Fellows, Brandon Kaplowitz, Christian Schröder de Witt, Shimon Whiteson
2024Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification.
Luyang Fang, Yongkai Chen, Wenxuan Zhong, Ping Ma
2024Bayesian Optimization of Function Networks with Partial Evaluations.
Poompol Buathong, Jiayue Wan, Raul Astudillo, Samuel Daulton, Maximilian Balandat, Peter I. Frazier
2024Bayesian Power Steering: An Effective Approach for Domain Adaptation of Diffusion Models.
Ding Huang, Ting Li, Jian Huang
2024Bayesian Program Learning by Decompiling Amortized Knowledge.
Alessandro B. Palmarini, Christopher G. Lucas, N. Siddharth
2024Bayesian Regret Minimization in Offline Bandits.
Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh
2024Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning.
Idan Achituve, Idit Diamant, Arnon Netzer, Gal Chechik, Ethan Fetaya
2024Be Your Own Neighborhood: Detecting Adversarial Examples by the Neighborhood Relations Built on Self-Supervised Learning.
Zhiyuan He, Yijun Yang, Pin-Yu Chen, Qiang Xu, Tsung-Yi Ho
2024Behavior Generation with Latent Actions.
Seungjae Lee, Yibin Wang, Haritheja Etukuru, H. Jin Kim, Nur Muhammad (Mahi) Shafiullah, Lerrel Pinto
2024BeigeMaps: Behavioral Eigenmaps for Reinforcement Learning from Images.
Sandesh Adhikary, Anqi Li, Byron Boots
2024Benchmarking Deletion Metrics with the Principled Explanations.
Yipei Wang, Xiaoqian Wang
2024Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT.
Jon Saad-Falcon, Daniel Y. Fu, Simran Arora, Neel Guha, Christopher Ré
2024Benign Overfitting in Adversarial Training of Neural Networks.
Yunjuan Wang, Kaibo Zhang, Raman Arora
2024Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data.
Xuran Meng, Difan Zou, Yuan Cao
2024Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models.
Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali K. Thabet, Albert Pumarola, Yaron Lipman
2024Best Arm Identification for Stochastic Rising Bandits.
Marco Mussi, Alessandro Montenegro, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli
2024Best of Both Worlds Guarantees for Smoothed Online Quadratic Optimization.
Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman
2024Better & Faster Large Language Models via Multi-token Prediction.
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Rozière, David Lopez-Paz, Gabriel Synnaeve
2024Better Locally Private Sparse Estimation Given Multiple Samples Per User.
Yuheng Ma, Ke Jia, Hanfang Yang
2024Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks.
Wenhan Yang, Jingdong Gao, Baharan Mirzasoleiman
2024BetterV: Controlled Verilog Generation with Discriminative Guidance.
Zehua Pei, Hui-Ling Zhen, Mingxuan Yuan, Yu Huang, Bei Yu
2024Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws.
Nikhil Sardana, Jacob P. Portes, Sasha Doubov, Jonathan Frankle
2024Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling.
Denis Blessing, Xiaogang Jia, Johannes Esslinger, Francisco Vargas, Gerhard Neumann
2024Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning.
Nikhil Vyas, Depen Morwani, Rosie Zhao, Gal Kaplun, Sham M. Kakade, Boaz Barak
2024Beyond Individual Input for Deep Anomaly Detection on Tabular Data.
Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan
2024Beyond Point Prediction: Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process.
Zichong Li, Qunzhi Xu, Zhenghao Xu, Yajun Mei, Tuo Zhao, Hongyuan Zha
2024Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains.
Levi E. Lingsch, Mike Yan Michelis, Emmanuel de Bézenac, Sirani M. Perera, Robert K. Katzschmann, Siddhartha Mishra
2024Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models.
Zhihe Lu, Jiawang Bai, Xin Li, Zeyu Xiao, Xinchao Wang
2024Beyond the Calibration Point: Mechanism Comparison in Differential Privacy.
Georgios Kaissis, Stefan Kolek, Borja Balle, Jamie Hayes, Daniel Rueckert
2024Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients.
Mengmeng Ma, Tang Li, Xi Peng
2024Beyond the Norms: Detecting Prediction Errors in Regression Models.
Andrés Altieri, Marco Romanelli, Georg Pichler, Florence Alberge, Pablo Piantanida
2024Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets.
Magzhan Gabidolla, Arman Zharmagambetov, Miguel Á. Carreira-Perpiñán
2024BiE: Bi-Exponent Block Floating-Point for Large Language Models Quantization.
Lancheng Zou, Wenqian Zhao, Shuo Yin, Chen Bai, Qi Sun, Bei Yu
2024BiLLM: Pushing the Limit of Post-Training Quantization for LLMs.
Wei Huang, Yangdong Liu, Haotong Qin, Ying Li, Shiming Zhang, Xianglong Liu, Michele Magno, Xiaojuan Qi
2024BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model.
Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, Han Liu
2024Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks.
Amit Peleg, Matthias Hein
2024Bidirectional Reciprocative Information Communication for Few-Shot Semantic Segmentation.
Yuanwei Liu, Junwei Han, Xiwen Yao, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Nian Liu, Fahad Shahbaz Khan
2024Bifurcated Attention for Single-Context Large-Batch Sampling.
Ben Athiwaratkun, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Haifeng Qian, Hantian Ding, Qing Sun, Jun Wang, Jiacheng Guo, Liangfu Chen, Parminder Bhatia, Ramesh Nallapati, Sudipta Sengupta, Bing Xiang
2024Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering.
Mitchell Black, Lucy Lin, Weng-Keen Wong, Amir Nayyeri
2024Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning.
Jun-Yi Hang, Min-Ling Zhang
2024Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains.
Kyungeun Lee, Ye Seul Sim, Hye-Seung Cho, Moonjung Eo, Suhee Yoon, Sanghyu Yoon, Woohyung Lim
2024Bipartite Matching in Massive Graphs: A Tight Analysis of EDCS.
Amir Azarmehr, Soheil Behnezhad, Mohammad Roghani
2024Bivariate Causal Discovery using Bayesian Model Selection.
Anish Dhir, Samuel Power, Mark van der Wilk
2024Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares.
Liangzu Peng, Wotao Yin
2024Boosting Offline Optimizers with Surrogate Sensitivity.
Manh Cuong Dao, Phi Le Nguyen, Truong Thao Nguyen, Trong Nghia Hoang
2024Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays.
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang
2024Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation.
Yaoming Wang, Jin Li, Wenrui Dai, Bowen Shi, Xiaopeng Zhang, Chenglin Li, Hongkai Xiong
2024Bootstrapping Fisher Market Equilibrium and First-Price Pacing Equilibrium.
Luofeng Liao, Christian Kroer
2024Borda Regret Minimization for Generalized Linear Dueling Bandits.
Yue Wu, Tao Jin, Qiwei Di, Hao Lou, Farzad Farnoud, Quanquan Gu
2024Bottleneck-Minimal Indexing for Generative Document Retrieval.
Xin Du, Lixin Xiu, Kumiko Tanaka-Ishii
2024Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints.
Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, Mina Konakovic-Lukovic
2024Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs.
Shenzhi Yang, Bin Liang, An Liu, Lin Gui, Xingkai Yao, Xiaofang Zhang
2024Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data.
Yvonne Zhou, Mingyu Liang, Ivan Brugere, Danial Dervovic, Antigoni Polychroniadou, Min Wu, Dana Dachman-Soled
2024Box Facets and Cut Facets of Lifted Multicut Polytopes.
Lucas Fabian Naumann, Jannik Irmai, Shengxian Zhao, Bjoern Andres
2024Boximator: Generating Rich and Controllable Motions for Video Synthesis.
Jiawei Wang, Yuchen Zhang, Jiaxin Zou, Yan Zeng, Guoqiang Wei, Liping Yuan, Hang Li
2024Breadth-First Exploration on Adaptive Grid for Reinforcement Learning.
Youngsik Yoon, Gangbok Lee, Sungsoo Ahn, Jungseul Ok
2024Break the Sequential Dependency of LLM Inference Using Lookahead Decoding.
Yichao Fu, Peter Bailis, Ion Stoica, Hao Zhang
2024Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents.
Chung-En Sun, Sicun Gao, Tsui-Wei Weng
2024Breaking through the learning plateaus of in-context learning in Transformer.
Jingwen Fu, Tao Yang, Yuwang Wang, Yan Lu, Nanning Zheng
2024Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning.
Xiyu Wang, Baijiong Lin, Daochang Liu, Ying-Cong Chen, Chang Xu
2024Bridging Environments and Language with Rendering Functions and Vision-Language Models.
Théo Cachet, Christopher R. Dance, Olivier Sigaud
2024Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses.
Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos, Mihalis Nicolaou, Yannis Panagakis
2024Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning.
Jiaqi Wang, Chenxu Zhao, Lingjuan Lyu, Quanzeng You, Mengdi Huai, Fenglong Ma
2024Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models.
Ludwig Winkler, Lorenz Richter, Manfred Opper
2024Bring Your Own (Non-Robust) Algorithm to Solve Robust MDPs by Estimating The Worst Kernel.
Uri Gadot, Kaixin Wang, Navdeep Kumar, Kfir Yehuda Levy, Shie Mannor
2024Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds.
Noémie Jaquier, Leonel Rozo, Miguel González Duque, Viacheslav Borovitskiy, Tamim Asfour
2024Building Socially-Equitable Public Models.
Yejia Liu, Jianyi Yang, Pengfei Li, Tongxin Li, Shaolei Ren
2024By Tying Embeddings You Are Assuming the Distributional Hypothesis.
Francesco Bertolotti, Walter Cazzola
2024ByMI: Byzantine Machine Identification with False Discovery Rate Control.
Chengde Qian, Mengyuan Wang, Haojie Ren, Changliang Zou
2024Byzantine Resilient and Fast Federated Few-Shot Learning.
Ankit Pratap Singh, Namrata Vaswani
2024Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates.
Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych
2024C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models.
Mintong Kang, Nezihe Merve Gürel, Ning Yu, Dawn Song, Bo Li
2024CARTE: Pretraining and Transfer for Tabular Learning.
Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux
2024CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables.
Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang
2024CCM: Real-Time Controllable Visual Content Creation Using Text-to-Image Consistency Models.
Jie Xiao, Kai Zhu, Han Zhang, Zhiheng Liu, Yujun Shen, Zhantao Yang, Ruili Feng, Yu Liu, Xueyang Fu, Zheng-Jun Zha
2024CF-OPT: Counterfactual Explanations for Structured Prediction.
Germain Vivier-Ardisson, Alexandre Forel, Axel Parmentier, Thibaut Vidal
2024CHAI: Clustered Head Attention for Efficient LLM Inference.
Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu
2024CHEMREASONER: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback.
Henry W. Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V. Olarte, Udishnu Sanyal, Conrad Johnston, Hongbin Liu, Heng Ji, Sutanay Choudhury
2024CKGConv: General Graph Convolution with Continuous Kernels.
Liheng Ma, Soumyasundar Pal, Yitian Zhang, Jiaming Zhou, Yingxue Zhang, Mark Coates
2024CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks.
Yulong Huang, Xiaopeng Lin, Hongwei Ren, Haotian Fu, Yue Zhou, Zunchang Liu, Biao Pan, Bojun Cheng
2024CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes.
Peter Mikhael, Itamar Chinn, Regina Barzilay
2024CLLMs: Consistency Large Language Models.
Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, Hao Zhang
2024COALA: A Practical and Vision-Centric Federated Learning Platform.
Weiming Zhuang, Jian Xu, Chen Chen, Jingtao Li, Lingjuan Lyu
2024COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability.
Xingang Guo, Fangxu Yu, Huan Zhang, Lianhui Qin, Bin Hu
2024COPAL: Continual Pruning in Large Language Generative Models.
Srikanth Malla, Joon Hee Choi, Chiho Choi
2024CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution.
Alex Gu, Baptiste Rozière, Hugh James Leather, Armando Solar-Lezama, Gabriel Synnaeve, Sida Wang
2024CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection.
Lin Zhu, Yifeng Yang, Qinying Gu, Xinbing Wang, Chenghu Zhou, Nanyang Ye
2024CW Complex Hypothesis for Image Data.
Yi Wang, Zhiren Wang
2024CaM: Cache Merging for Memory-efficient LLMs Inference.
Yuxin Zhang, Yuxuan Du, Gen Luo, Yunshan Zhong, Zhenyu Zhang, Shiwei Liu, Rongrong Ji
2024CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources.
Sikha Pentyala, Mayana Pereira, Martine De Cock
2024CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process.
Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang
2024Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling.
Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov
2024Calibration Bottleneck: Over-compressed Representations are Less Calibratable.
Deng-Bao Wang, Min-Ling Zhang
2024Can AI Assistants Know What They Don't Know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu, Wenwei Zhang, Zhangyue Yin, Shimin Li, Linyang Li, Zhengfu He, Kai Chen, Xipeng Qiu
2024Can Gaussian Sketching Converge Faster on a Preconditioned Landscape?
Yilong Wang, Haishan Ye, Guang Dai, Ivor W. Tsang
2024Can Implicit Bias Imply Adversarial Robustness?
Hancheng Min, René Vidal
2024Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar
2024Can Machines Learn the True Probabilities?
Jinsook Kim
2024Can Mamba Learn How To Learn? A Comparative Study on In-Context Learning Tasks.
Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos
2024Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective.
Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani
2024Can a Few Decide for Many? The Metric Distortion of Sortition.
Ioannis Caragiannis, Evi Micha, Jannik Peters
2024Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data.
Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng
2024CarbonNovo: Joint Design of Protein Structure and Sequence Using a Unified Energy-based Model.
Milong Ren, Tian Zhu, Haicang Zhang
2024Careful with that Scalpel: Improving Gradient Surgery with an EMA.
Yu-Guan Hsieh, James Thornton, Eugène Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin
2024CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling.
Junchao Gong, Lei Bai, Peng Ye, Wanghan Xu, Na Liu, Jianhua Dai, Xiaokang Yang, Wanli Ouyang
2024Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation.
Yunheng Li, Zhong-Yu Li, Quan-Sheng Zeng, Qibin Hou, Ming-Ming Cheng
2024Case-Based or Rule-Based: How Do Transformers Do the Math?
Yi Hu, Xiaojuan Tang, Haotong Yang, Muhan Zhang
2024Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning.
Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin
2024Category-Aware Active Domain Adaptation.
Wenxiao Xiao, Jiuxiang Gu, Hongfu Liu
2024CauDiTS: Causal Disentangled Domain Adaptation of Multivariate Time Series.
Junxin Lu, Shiliang Sun
2024Causal Action Influence Aware Counterfactual Data Augmentation.
Núria Armengol Urpí, Marco Bagatella, Marin Vlastelica, Georg Martius
2024Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals.
Ziyi Liu, Idan Attias, Daniel M. Roy
2024Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model.
Chenyin Gao, Zhiming Zhang, Shu Yang
2024Causal Discovery via Conditional Independence Testing with Proxy Variables.
Mingzhou Liu, Xinwei Sun, Yu Qiao, Yizhou Wang
2024Causal Discovery with Fewer Conditional Independence Tests.
Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
2024Causal Effect Identification in LiNGAM Models with Latent Confounders.
Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash
2024Causal Inference from Competing Treatments.
Ana-Andreea Stoica, Vivian Y. Nastl, Moritz Hardt
2024Causal Inference out of Control: Estimating Performativity without Treatment Randomization.
Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner
2024Causal Representation Learning Made Identifiable by Grouping of Observational Variables.
Hiroshi Morioka, Aapo Hyvärinen
2024Causal Representation Learning from Multiple Distributions: A General Setting.
Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng
2024Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference.
Yan Zhong, Xingyu Wu, Li Zhang, Chenxi Yang, Tingting Jiang
2024Causality Based Front-door Defense Against Backdoor Attack on Language Models.
Yiran Liu, Xiaoang Xu, Zhiyi Hou, Yang Yu
2024Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization.
Xueyang Tang, Song Guo, Jingcai Guo, Jie Zhang, Yue Yu
2024Cell2Sentence: Teaching Large Language Models the Language of Biology.
Daniel LeVine, Syed Asad Rizvi, Sacha Lévy, Nazreen Pallikkavaliyaveetil, David Zhang, Xingyu Chen, Sina Ghadermarzi, Ruiming Wu, Zihe Zheng, Ivan Vrkic, Anna Zhong, Daphne Raskin, Insu Han, Antonio Henrique de Oliveira Fonseca, Josue Ortega Caro, Amin Karbasi, Rahul Madhav Dhodapkar, David van Dijk
2024Centralized Selection with Preferences in the Presence of Biases.
L. Elisa Celis, Amit Kumar, Nisheeth K. Vishnoi, Andrew Xu
2024Certifiably Byzantine-Robust Federated Conformal Prediction.
Mintong Kang, Zhen Lin, Jimeng Sun, Cao Xiao, Bo Li
2024Chain of Code: Reasoning with a Language Model-Augmented Code Emulator.
Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, Brian Ichter
2024Chain-of-Thought Predictive Control.
Zhiwei Jia, Vineet Thumuluri, Fangchen Liu, Linghao Chen, Zhiao Huang, Hao Su
2024Challenges and Considerations in the Evaluation of Bayesian Causal Discovery.
Amir Mohammad Karimi-Mamaghan, Panagiotis Tigas, Karl Henrik Johansson, Yarin Gal, Yashas Annadani, Stefan Bauer
2024Challenges in Training PINNs: A Loss Landscape Perspective.
Pratik Rathore, Weimu Lei, Zachary Frangella, Lu Lu, Madeleine Udell
2024Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale.
Candi Zheng, Yuan Lan
2024Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation.
Randall Balestriero, Romain Cosentino, Sarath Shekkizhar
2024Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum.
Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, David Belius
2024Characterizing ResNet's Universal Approximation Capability.
Chenghao Liu, Enming Liang, Minghua Chen
2024Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension.
Fan Yin, Jayanth Srinivasa, Kai-Wei Chang
2024Chasing Convex Functions with Long-term Constraints.
Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant J. Shenoy
2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference.
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael I. Jordan, Joseph E. Gonzalez, Ion Stoica
2024Class-Imbalanced Graph Learning without Class Rebalancing.
Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong
2024Classification Under Strategic Self-Selection.
Guy Horowitz, Yonatan Sommer, Moran Koren, Nir Rosenfeld
2024Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference.
Luca Masserano, Alexander Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee
2024Clifford-Steerable Convolutional Neural Networks.
Maksim Zhdanov, David Ruhe, Maurice Weiler, Ana Lucic, Johannes Brandstetter, Patrick Forré
2024Closing the Gap: Achieving Global Convergence (Last Iterate) of Actor-Critic under Markovian Sampling with Neural Network Parametrization.
Mudit Gaur, Amrit S. Bedi, Di Wang, Vaneet Aggarwal
2024Cluster-Aware Similarity Diffusion for Instance Retrieval.
Jifei Luo, Hantao Yao, Changsheng Xu
2024Clustered Federated Learning via Gradient-based Partitioning.
Heasung Kim, Hyeji Kim, Gustavo de Veciana
2024CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations.
Jules Berman, Benjamin Peherstorfer
2024Coactive Learning for Large Language Models using Implicit User Feedback.
Aaron David Tucker, Kianté Brantley, Adam Cahall, Thorsten Joachims
2024Coarse-To-Fine Tensor Trains for Compact Visual Representations.
Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen, Serge J. Belongie, Michael J. Kastoryano, Sagie Benaim
2024Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models.
Qitan Lv, Jie Wang, Hanzhu Chen, Bin Li, Yongdong Zhang, Feng Wu
2024Code as Reward: Empowering Reinforcement Learning with VLMs.
David Venuto, Mohammad Sami Nur Islam, Martin Klissarov, Doina Precup, Sherry Yang, Ankit Anand
2024CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay.
Natasha Butt, Blazej Manczak, Auke J. Wiggers, Corrado Rainone, David W. Zhang, Michaël Defferrard, Taco Cohen
2024Codebook Features: Sparse and Discrete Interpretability for Neural Networks.
Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman
2024CogBench: a large language model walks into a psychology lab.
Julian Coda-Forno, Marcel Binz, Jane X. Wang, Eric Schulz
2024CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding.
Kaiyuan Chen, Xingzhuo Guo, Yu Zhang, Jianmin Wang, Mingsheng Long
2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis.
Tianyu Guo, Sai Praneeth Karimireddy, Michael I. Jordan
2024Collaborative Learning with Different Labeling Functions.
Yuyang Deng, Mingda Qiao
2024Collage: Light-Weight Low-Precision Strategy for LLM Training.
Tao Yu, Gaurav Gupta, Karthick Gopalswamy, Amith R. Mamidala, Hao Zhou, Jeffrey Huynh, Youngsuk Park, Ron Diamant, Anoop Deoras, Luke Huan
2024Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval.
Qiwei Tian, Chenhao Lin, Zhengyu Zhao, Qian Li, Chao Shen
2024Collective Certified Robustness against Graph Injection Attacks.
Yuni Lai, Bailin Pan, Kaihuang Chen, Yancheng Yuan, Kai Zhou
2024Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better.
Vicente Balmaseda, Ying Xu, Yixin Cao, Nate Veldt
2024Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond.
Xutong Liu, Siwei Wang, Jinhang Zuo, Han Zhong, Xuchuang Wang, Zhiyong Wang, Shuai Li, Mohammad Hajiesmaili, John C. S. Lui, Wei Chen
2024Combining Experimental and Historical Data for Policy Evaluation.
Ting Li, Chengchun Shi, Qianglin Wen, Yang Sui, Yongli Qin, Chunbo Lai, Hongtu Zhu
2024Community-Invariant Graph Contrastive Learning.
Shiyin Tan, Dongyuan Li, Renhe Jiang, Ying Zhang, Manabu Okumura
2024Compact Optimality Verification for Optimization Proxies.
Wenbo Chen, Haoruo Zhao, Mathieu Tanneau, Pascal Van Hentenryck
2024Comparing Graph Transformers via Positional Encodings.
Mitchell Black, Zhengchao Wan, Gal Mishne, Amir Nayyeri, Yusu Wang
2024CompeteAI: Understanding the Competition Dynamics of Large Language Model-based Agents.
Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, Xing Xie
2024Completing Visual Objects via Bridging Generation and Segmentation.
Xiang Li, Yinpeng Chen, Chung-Ching Lin, Hao Chen, Kai Hu, Rita Singh, Bhiksha Raj, Lijuan Wang, Zicheng Liu
2024Complexity Matters: Feature Learning in the Presence of Spurious Correlations.
Guanwen Qiu, Da Kuang, Surbhi Goel
2024Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks.
Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka
2024Compositional Curvature Bounds for Deep Neural Networks.
Taha Entesari, Sina Sharifi, Mahyar Fazlyab
2024Compositional Few-Shot Class-Incremental Learning.
Yixiong Zou, Shanghang Zhang, Haichen Zhou, Yuhua Li, Ruixuan Li
2024Compositional Image Decomposition with Diffusion Models.
Jocelin Su, Nan Liu, Yanbo Wang, Joshua B. Tenenbaum, Yilun Du
2024Compositional Text-to-Image Generation with Dense Blob Representations.
Weili Nie, Sifei Liu, Morteza Mardani, Chao Liu, Benjamin Eckart, Arash Vahdat
2024Compress Clean Signal from Noisy Raw Image: A Self-Supervised Approach.
Zhihao Li, Yufei Wang, Alex C. Kot, Bihan Wen
2024Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation.
Can Yaras, Peng Wang, Laura Balzano, Qing Qu
2024Compressing Large Language Models by Joint Sparsification and Quantization.
Jinyang Guo, Jianyu Wu, Zining Wang, Jiaheng Liu, Ge Yang, Yifu Ding, Ruihao Gong, Haotong Qin, Xianglong Liu
2024Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth.
Kevin Kögler, Aleksandr Shevchenko, Hamed Hassani, Marco Mondelli
2024Compute Better Spent: Replacing Dense Layers with Structured Matrices.
Shikai Qiu, Andres Potapczynski, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
2024ConTextual: Evaluating Context-Sensitive Text-Rich Visual Reasoning in Large Multimodal Models.
Rohan Wadhawan, Hritik Bansal, Kai-Wei Chang, Nanyun Peng
2024Concentration Inequalities for General Functions of Heavy-Tailed Random Variables.
Shaojie Li, Yong Liu
2024Conditional Common Entropy for Instrumental Variable Testing and Partial Identification.
Ziwei Jiang, Murat Kocaoglu
2024Conditional Language Learning with Context.
Xiao Zhang, Miao Li, Ji Wu
2024Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations.
Henrik Schopmans, Pascal Friederich
2024Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation.
Yididiya Y. Nadew, Xuhui Fan, Christopher John Quinn
2024Confidence Aware Inverse Constrained Reinforcement Learning.
Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart
2024Confidence-aware Contrastive Learning for Selective Classification.
Yu-Chang Wu, Shen-Huan Lyu, Haopu Shang, Xiangyu Wang, Chao Qian
2024Configurable Mirror Descent: Towards a Unification of Decision Making.
Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Shuyue Hu, Xiao Huang, Hau Chan, Bo An
2024Conformal Prediction Sets Improve Human Decision Making.
Jesse C. Cresswell, Yi Sui, Bhargava Kumar, Noël Vouitsis
2024Conformal Prediction for Deep Classifier via Label Ranking.
Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei
2024Conformal Prediction with Learned Features.
Shayan Kiyani, George J. Pappas, Hamed Hassani
2024Conformal Predictions under Markovian Data.
Frédéric Zheng, Alexandre Proutière
2024Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them).
Drew Prinster, Samuel Don Stanton, Anqi Liu, Suchi Saria
2024Conformal prediction for multi-dimensional time series by ellipsoidal sets.
Chen Xu, Hanyang Jiang, Yao Xie
2024Conformalized Adaptive Forecasting of Heterogeneous Trajectories.
Yanfei Zhou, Lars Lindemann, Matteo Sesia
2024Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration.
Shiang Qi, Yakun Yu, Russell Greiner
2024Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases.
Ziyi Zhang, Sen Zhang, Yibing Zhan, Yong Luo, Yonggang Wen, Dacheng Tao
2024Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations.
Helen Qu, Sang Michael Xie
2024Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models.
Zhengbo Wang, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
2024Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
2024Consistent Adversarially Robust Linear Classification: Non-Parametric Setting.
Elvis Dohmatob
2024Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data.
Giannis Daras, Alex Dimakis, Constantinos Daskalakis
2024Consistent Long-Term Forecasting of Ergodic Dynamical Systems.
Vladimir R. Kostic, Karim Lounici, Prune Inzerilli, Pietro Novelli, Massimiliano Pontil
2024Consistent Submodular Maximization.
Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
2024Constrained Ensemble Exploration for Unsupervised Skill Discovery.
Chenjia Bai, Rushuai Yang, Qiaosheng Zhang, Kang Xu, Yi Chen, Ting Xiao, Xuelong Li
2024Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics.
Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin
2024Constrained Reinforcement Learning Under Model Mismatch.
Zhongchang Sun, Sihong He, Fei Miao, Shaofeng Zou
2024ContPhy: Continuum Physical Concept Learning and Reasoning from Videos.
Zhicheng Zheng, Xin Yan, Zhenfang Chen, Jingzhou Wang, Qin Zhi Eddie Lim, Joshua B. Tenenbaum, Chuang Gan
2024Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous Data.
Wenxi Lv, Qinliang Su, Hai Wan, Hongteng Xu, Wenchao Xu
2024Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design.
Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh
2024Contextual Feature Selection with Conditional Stochastic Gates.
Ram Dyuthi Sristi, Ofir Lindenbaum, Shira Lifshitz, Maria Lavzin, Jackie Schiller, Gal Mishne, Hadas Benisty
2024Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning.
Jannik Deuschel, Caleb Ellington, Yingtao Luo, Benjamin J. Lengerich, Pascal Friederich, Eric P. Xing
2024Continuous Treatment Effects with Surrogate Outcomes.
Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki, Ryan A. Rossi, Ritwik Sinha, Edward H. Kennedy
2024Contrasting Multiple Representations with the Multi-Marginal Matching Gap.
Zoe Piran, Michal Klein, James Thornton, Marco Cuturi
2024Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources.
Meng Xia, Jonathan Wilson, Benjamin Goldstein, Ricardo Henao
2024Contrastive Predict-and-Search for Mixed Integer Linear Programs.
Taoan Huang, Aaron M. Ferber, Arman Zharmagambetov, Yuandong Tian, Bistra Dilkina
2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation.
Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, Young Jin Kim
2024Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning.
Xiaoyu Wen, Chenjia Bai, Kang Xu, Xudong Yu, Yang Zhang, Xuelong Li, Zhen Wang
2024Controllable Prompt Tuning For Balancing Group Distributional Robustness.
Hoang Phan, Andrew Gordon Wilson, Qi Lei
2024Controlled Decoding from Language Models.
Sidharth Mudgal, Jong Lee, Harish Ganapathy, Yaguang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Heng-Tze Cheng, Michael Collins, Trevor Strohman, Jilin Chen, Alex Beutel, Ahmad Beirami
2024Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning.
Matteo Bettini, Ryan Kortvelesy, Amanda Prorok
2024ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy.
Kirill Vishniakov, Zhiqiang Shen, Zhuang Liu
2024Convergence Guarantees for the DeepWalk Embedding on Block Models.
Christopher Harker, Aditya Bhaskara
2024Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms.
Yuchen Li, Laura Balzano, Deanna Needell, Hanbaek Lyu
2024Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point.
David Martínez-Rubio, Christophe Roux, Sebastian Pokutta
2024Convergence of Online Learning Algorithm for a Mixture of Multiple Linear Regressions.
Yujing Liu, Zhixin Liu, Lei Guo
2024Convergence of Some Convex Message Passing Algorithms to a Fixed Point.
Václav Vorácek, Tomás Werner
2024Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption.
Itamar Zimerman, Moran Baruch, Nir Drucker, Gilad Ezov, Omri Soceanu, Lior Wolf
2024Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time.
Sungyoon Kim, Mert Pilanci
2024Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning.
Charles Andrew Dickens, Changyu Gao, Connor Pryor, Stephen J. Wright, Lise Getoor
2024Cooperative Graph Neural Networks.
Ben Finkelshtein, Xingyue Huang, Michael M. Bronstein, Ismail Ilkan Ceylan
2024Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation.
Michelle Pan, Mariah L. Schrum, Vivek Myers, Erdem Biyik, Anca D. Dragan
2024Copula-Nested Spectral Kernel Network.
Jinyue Tian, Hui Xue, Yanfang Xue, Pengfei Fang
2024Copyright Traps for Large Language Models.
Matthieu Meeus, Igor Shilov, Manuel Faysse, Yves-Alexandre de Montjoye
2024Coresets for Multiple ℓp Regression.
David P. Woodruff, Taisuke Yasuda
2024Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder.
Yiyang Ma, Wenhan Yang, Jiaying Liu
2024Correlation-Induced Label Prior for Semi-Supervised Multi-Label Learning.
Biao Liu, Ning Xu, Xiangyu Fang, Xin Geng
2024CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks.
Shashank Agnihotri, Steffen Jung, Margret Keuper
2024Counterfactual Image Editing.
Yushu Pan, Elias Bareinboim
2024Counterfactual Metarules for Local and Global Recourse.
Tom Bewley, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni, Manuela Veloso
2024Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training.
Ming-Kun Xie, Jiahao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
2024Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation.
Danny Halawi, Alexander Wei, Eric Wallace, Tony Tong Wang, Nika Haghtalab, Jacob Steinhardt
2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning.
Michael T. Matthews, Michael Beukman, Benjamin Ellis, Mikayel Samvelyan, Matthew Thomas Jackson, Samuel Coward, Jakob Nicolaus Foerster
2024Creative Text-to-Audio Generation via Synthesizer Programming.
Manuel Cherep, Nikhil Singh, Jessica Shand
2024Criterion Collapse and Loss Distribution Control.
Matthew J. Holland
2024Critical feature learning in deep neural networks.
Kirsten Fischer, Javed Lindner, David Dahmen, Zohar Ringel, Michael Krämer, Moritz Helias
2024Critical windows: non-asymptotic theory for feature emergence in diffusion models.
Marvin Li, Sitan Chen
2024Cross-Domain Policy Adaptation by Capturing Representation Mismatch.
Jiafei Lyu, Chenjia Bai, Jingwen Yang, Zongqing Lu, Xiu Li
2024Cross-domain Open-world Discovery.
Shuo Wen, Maria Brbic
2024Cross-view Masked Diffusion Transformers for Person Image Synthesis.
Trung X. Pham, Kang Zhang, Chang D. Yoo
2024CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers.
Dachuan Shi, Chaofan Tao, Anyi Rao, Zhendong Yang, Chun Yuan, Jiaqi Wang
2024CuTS: Customizable Tabular Synthetic Data Generation.
Mark Vero, Mislav Balunovic, Martin T. Vechev
2024CurBench: Curriculum Learning Benchmark.
Yuwei Zhou, Zirui Pan, Xin Wang, Hong Chen, Haoyang Li, Yanwen Huang, Zhixiao Xiong, Fangzhou Xiong, Peiyang Xu, Shengnan Liu, Wenwu Zhu
2024Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes.
Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar
2024D-Flow: Differentiating through Flows for Controlled Generation.
Heli Ben-Hamu, Omri Puny, Itai Gat, Brian Karrer, Uriel Singer, Yaron Lipman
2024DAG-Based Column Generation for Adversarial Team Games.
Youzhi Zhang, Bo An, Daniel Dajun Zeng
2024DE-COP: Detecting Copyrighted Content in Language Models Training Data.
André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li
2024DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automaton.
Yiyou Sun, Junjie Hu, Wei Cheng, Haifeng Chen
2024DFD: Distilling the Feature Disparity Differently for Detectors.
Kang Liu, Yingyi Zhang, Jingyun Zhang, Jinmin Li, Jun Wang, Shaoming Wang, Chun Yuan, Rizen Guo
2024DFlow: A Generative Model Combining Denoising AutoEncoder and Normalizing Flow for High Fidelity Waveform Generation.
Chenfeng Miao, Qingying Zhu, Minchuan Chen, Wei Hu, Zijian Li, Shaojun Wang, Jing Xiao
2024DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation.
Jinxin Liu, Xinghong Guo, Zifeng Zhuang, Donglin Wang
2024DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation.
Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J. Getzen, Qi Long, Mayur Naik, Ravi B. Parikh, Eric Wong
2024DITTO: Diffusion Inference-Time T-Optimization for Music Generation.
Zachary Novack, Julian J. McAuley, Taylor Berg-Kirkpatrick, Nicholas J. Bryan
2024DMTG: One-Shot Differentiable Multi-Task Grouping.
Yuan Gao, Shuguo Jiang, Moran Li, Jin-Gang Yu, Gui-Song Xia
2024DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation.
Qinshuo Liu, Zixin Wang, Xi-An Li, Xinyao Ji, Lei Zhang, Lin Liu, Zhonghua Liu
2024DNCs Require More Planning Steps.
Yara Shamshoum, Nitzan Hodos, Yuval Sieradzki, Assaf Schuster
2024DOGE: Domain Reweighting with Generalization Estimation.
Simin Fan, Matteo Pagliardini, Martin Jaggi
2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems.
Zhi Zheng, Shunyu Yao, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Ke Tang
2024DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training.
Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu
2024DPZero: Private Fine-Tuning of Language Models without Backpropagation.
Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil, Sewoong Oh, Niao He
2024DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated Images.
Baoying Chen, Jishen Zeng, Jianquan Yang, Rui Yang
2024DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design.
Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht
2024DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning.
Siyuan Guo, Cheng Deng, Ying Wen, Hechang Chen, Yi Chang, Jun Wang
2024DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection.
Yongchao Feng, Shiwei Li, Yingjie Gao, Ziyue Huang, Yanan Zhang, Qingjie Liu, Yunhong Wang
2024DUPLEX: Dual GAT for Complex Embedding of Directed Graphs.
Zhaoru Ke, Hang Yu, Jianguo Li, Haipeng Zhang
2024Data Engineering for Scaling Language Models to 128K Context.
Yao Fu, Rameswar Panda, Xinyao Niu, Xiang Yue, Hannaneh Hajishirzi, Yoon Kim, Hao Peng
2024Data Poisoning Attacks against Conformal Prediction.
Yangyi Li, Aobo Chen, Wei Qian, Chenxu Zhao, Divya Lidder, Mengdi Huai
2024Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond.
Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder
2024Data-Efficient Molecular Generation with Hierarchical Textual Inversion.
Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin
2024Data-efficient Large Vision Models through Sequential Autoregression.
Zhiwei Hao, Jianyuan Guo, Chengcheng Wang, Yehui Tang, Han Wu, Han Hu, Kai Han, Chang Xu
2024Data-free Distillation of Diffusion Models with Bootstrapping.
Jiatao Gu, Chen Wang, Shuangfei Zhai, Yizhe Zhang, Lingjie Liu, Joshua M. Susskind
2024Data-free Neural Representation Compression with Riemannian Neural Dynamics.
Zhengqi Pei, Anran Zhang, Shuhui Wang, Xiangyang Ji, Qingming Huang
2024DataFreeShield: Defending Adversarial Attacks without Training Data.
Hyeyoon Lee, Kanghyun Choi, Dain Kwon, Sunjong Park, Mayoore Selvarasa Jaiswal, Noseong Park, Jonghyun Choi, Jinho Lee
2024DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection.
Zhi Zhou, Ming Yang, Jiang-Xin Shi, Lan-Zhe Guo, Yufeng Li
2024Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization.
Gergely Neu, Nneka Okolo
2024Debating with More Persuasive LLMs Leads to More Truthful Answers.
Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, Ethan Perez
2024Debiased Distribution Compression.
Lingxiao Li, Raaz Dwivedi, Lester Mackey
2024Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics.
Xinyu Zhang, Wenjie Qiu, Yi-Chen Li, Lei Yuan, Chengxing Jia, Zongzhang Zhang, Yang Yu
2024Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers.
Yuxing Liu, Lesi Chen, Luo Luo
2024Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective.
Cheng Tan, Zhangyang Gao, Hanqun Cao, Xingran Chen, Ge Wang, Lirong Wu, Jun Xia, Jiangbin Zheng, Stan Z. Li
2024DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning.
Jianxiong Li, Jinliang Zheng, Yinan Zheng, Liyuan Mao, Xiao Hu, Sijie Cheng, Haoyi Niu, Jihao Liu, Yu Liu, Jingjing Liu, Ya-Qin Zhang, Xianyuan Zhan
2024Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.
Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian R. Bartoldson, Ajay Kumar Jaiswal, Kaidi Xu, Bhavya Kailkhura, Dan Hendrycks, Dawn Song, Zhangyang Wang, Bo Li
2024Decoding-time Realignment of Language Models.
Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares-López, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
2024Decomposable Submodular Maximization in Federated Setting.
Akbar Rafiey
2024Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling.
Bairu Hou, Yujian Liu, Kaizhi Qian, Jacob Andreas, Shiyu Chang, Yang Zhang
2024Decomposing and Editing Predictions by Modeling Model Computation.
Harshay Shah, Andrew Ilyas, Aleksander Madry
2024Deconstructing the Goldilocks Zone of Neural Network Initialization.
Artem Vysogorets, Anna Dawid, Julia Kempe
2024Decouple then Classify: A Dynamic Multi-view Labeling Strategy with Shared and Specific Information.
Xinhang Wan, Jiyuan Liu, Xinwang Liu, Yi Wen, Hao Yu, Siwei Wang, Shengju Yu, Tianjiao Wan, Jun Wang, En Zhu
2024Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks.
Mikkel Jordahn, Pablo M. Olmos
2024Decoupling Learning and Decision-Making: Breaking the O(T) Barrier in Online Resource Allocation with First-Order Methods.
Wenzhi Gao, Chunlin Sun, Chenyu Xue, Yinyu Ye
2024Deep Demonstration Tracing: Learning Generalizable Imitator Policy for Runtime Imitation from a Single Demonstration.
Xiong-Hui Chen, Junyin Ye, Hang Zhao, Yi-Chen Li, Xuhui Liu, Haoran Shi, Yu-Yan Xu, Zhihao Ye, Si-Hang Yang, Yang Yu, Anqi Huang, Kai Xu, Zongzhang Zhang
2024Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures.
Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang, Feng Zhou, Robert C. Qiu, Zhenyu Liao
2024Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization.
Yirui Liu, Xinghao Qiao, Yulong Pei, Liying Wang
2024Deep Fusion: Efficient Network Training via Pre-trained Initializations.
Hanna Mazzawi, Javier Gonzalvo, Michael Wunder, Sammy Jerome, Benoit Dherin
2024Deep Networks Always Grok and Here is Why.
Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk
2024Deep Neural Room Acoustics Primitive.
Yuhang He, Anoop Cherian, Gordon Wichern, Andrew Markham
2024Deep Regression Representation Learning with Topology.
Shihao Zhang, Kenji Kawaguchi, Angela Yao
2024Deep Stochastic Mechanics.
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
2024DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning.
S. Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim, Sewoong Oh, Pramod Viswanath
2024Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss.
Yahong Yang, Juncai He
2024Defense against Backdoor Attack on Pre-trained Language Models via Head Pruning and Attention Normalization.
Xingyi Zhao, Depeng Xu, Shuhan Yuan
2024Defense against Model Extraction Attack by Bayesian Active Watermarking.
Zhenyi Wang, Yihan Wu, Heng Huang
2024Defining Neural Network Architecture through Polytope Structures of Datasets.
Sangmin Lee, Abbas Mammadov, Jong Chul Ye
2024Degeneration-free Policy Optimization: RL Fine-Tuning for Language Models without Degeneration.
Youngsoo Jang, Geon-Hyeong Kim, Byoungjip Kim, Yu Jin Kim, Honglak Lee, Moontae Lee
2024Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation.
Hugo Attali, Davide Buscaldi, Nathalie Pernelle
2024Deletion-Anticipative Data Selection with a Limited Budget.
Rachael Hwee Ling Sim, Jue Fan, Xiao Tian, Patrick Jaillet, Bryan Kian Hsiang Low
2024Delving into Differentially Private Transformer.
Youlong Ding, Xueyang Wu, Yining Meng, Yonggang Luo, Hao Wang, Weike Pan
2024Delving into the Convergence of Generalized Smooth Minimax Optimization.
Wenhan Xian, Ziyi Chen, Heng Huang
2024Demystifying SGD with Doubly Stochastic Gradients.
Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner
2024Denoising Autoregressive Representation Learning.
Yazhe Li, Jörg Bornschein, Ting Chen
2024Dense Reward for Free in Reinforcement Learning from Human Feedback.
Alex James Chan, Hao Sun, Samuel Holt, Mihaela van der Schaar
2024Density Ratio Estimation with Doubly Strong Robustness.
Ryosuke Nagumo, Hironori Fujisawa
2024Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts.
Ha Manh Bui, Anqi Liu
2024Designing Decision Support Systems using Counterfactual Prediction Sets.
Eleni Straitouri, Manuel Gomez Rodriguez
2024DetKDS: Knowledge Distillation Search for Object Detectors.
Lujun Li, Yufan Bao, Peijie Dong, Chuanguang Yang, Anggeng Li, Wenhan Luo, Qifeng Liu, Wei Xue, Yike Guo
2024Detecting Any instruction-to-answer interaction relationship: Universal Instruction-to-Answer Navigator for Med-VQA.
Zhongze Wu, Hongyan Xu, Yitian Long, Shan You, Xiu Su, Jun Long, Yueyi Luo, Chang Xu
2024Detecting Influence Structures in Multi-Agent Reinforcement Learning.
Fabian Raoul Pieroth, Katherine E. Fitch, Lenz Belzner
2024Detecting and Identifying Selection Structure in Sequential Data.
Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang
2024DiJiang: Efficient Large Language Models through Compact Kernelization.
Hanting Chen, Liuzhi Cheng, Xutao Wang, Yuchuan Tian, Yunhe Wang
2024DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models.
Sidi Lu, Wenbo Zhao, Chenyang Tao, Arpit Gupta, Shanchan Wu, Tagyoung Chung, Nanyun Peng
2024Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View.
Jin Wang, Shichao Dong, Yapeng Zhu, Kelu Yao, Weidong Zhao, Chao Li, Ping Luo
2024DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation.
Zelin Zang, Hao Luo, Kai Wang, Panpan Zhang, Fan Wang, Stan Z. Li, Yang You
2024DiffDA: a Diffusion model for weather-scale Data Assimilation.
Langwen Huang, Lukas Gianinazzi, Yuejiang Yu, Peter D. Düben, Torsten Hoefler
2024DiffFPR: Diffusion Prior for Oversampled Fourier Phase Retrieval.
Ji Li, Chao Wang
2024DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching.
Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, Weinan Zhang
2024Differentiability and Optimization of Multiparameter Persistent Homology.
Luis Scoccola, Siddharth Setlur, David Loiseaux, Mathieu Carrière, Steve Oudot
2024Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution.
Johannes Zenn, Robert Bamler
2024Differentiable Combinatorial Scheduling at Scale.
Mingju Liu, Yingjie Li, Jiaqi Yin, Zhiru Zhang, Cunxi Yu
2024Differentiable Distributionally Robust Optimization Layers.
Xutao Ma, Chao Ning, Wenli Du
2024Differentiable Mapper for Topological Optimization of Data Representation.
Ziyad Oulhaj, Mathieu Carrière, Bertrand Michel
2024Differentiable Model Scaling using Differentiable Topk.
Kai Liu, Ruohui Wang, Jianfei Gao, Kai Chen
2024Differentiable Weightless Neural Networks.
Alan Tendler Leibel Bacellar, Zachary Susskind, Maurício Breternitz Jr., Eugene John, Lizy Kurian John, Priscila Machado Vieira Lima, Felipe M. G. França
2024Differentially Private Bias-Term Fine-tuning of Foundation Models.
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis
2024Differentially Private Decentralized Learning with Random Walks.
Edwige Cyffers, Aurélien Bellet, Jalaj Upadhyay
2024Differentially Private Domain Adaptation with Theoretical Guarantees.
Raef Bassily, Corinna Cortes, Anqi Mao, Mehryar Mohri
2024Differentially Private Post-Processing for Fair Regression.
Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
2024Differentially Private Representation Learning via Image Captioning.
Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024Differentially Private Sum-Product Networks.
Xenia Heilmann, Mattia Cerrato, Ernst Althaus
2024Differentially Private Synthetic Data via Foundation Model APIs 2: Text.
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
2024Differentially Private Worst-group Risk Minimization.
Xinyu Zhou, Raef Bassily
2024Differentially private exact recovery for stochastic block models.
Dung Nguyen, Anil Kumar S. Vullikanti
2024Diffuse, Sample, Project: Plug-And-Play Controllable Graph Generation.
Kartik Sharma, Srijan Kumar, Rakshit S. Trivedi
2024Diffusion Language Models Are Versatile Protein Learners.
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
2024Diffusion Model-Augmented Behavioral Cloning.
Shang-Fu Chen, Hsiang-Chun Wang, Ming-Hao Hsu, Chun-Mao Lai, Shao-Hua Sun
2024Diffusion Models Demand Contrastive Guidance for Adversarial Purification to Advance.
Mingyuan Bai, Wei Huang, Tenghui Li, Andong Wang, Junbin Gao, Cesar F. Caiafa, Qibin Zhao
2024Diffusion Models Encode the Intrinsic Dimension of Data Manifolds.
Jan Stanczuk, Georgios Batzolis, Teo Deveney, Carola-Bibiane Schönlieb
2024Diffusion Posterior Sampling is Computationally Intractable.
Shivam Gupta, Ajil Jalal, Aditya Parulekar, Eric Price, Zhiyang Xun
2024Diffusion Rejection Sampling.
Byeonghu Na, Yeongmin Kim, Minsang Park, DongHyeok Shin, Wanmo Kang, Il-Chul Moon
2024Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations.
Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens
2024Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering.
Jie Wen, Shijie Deng, Waikeung Wong, Guoqing Chao, Chao Huang, Lunke Fei, Yong Xu
2024Diffusive Gibbs Sampling.
Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
2024DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency.
Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi
2024Directly Denoising Diffusion Models.
Dan Zhang, Jingjing Wang, Feng Luo
2024Dirichlet Flow Matching with Applications to DNA Sequence Design.
Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, Tommi S. Jaakkola
2024DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents.
Yilun Xu, Gabriele Corso, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis
2024Discounted Adaptive Online Learning: Towards Better Regularization.
Zhiyu Zhang, David Bombara, Heng Yang
2024Discovering Bias in Latent Space: An Unsupervised Debiasing Approach.
Dyah Adila, Shuai Zhang, Boran Han, Bernie Wang
2024Discovering Environments with XRM.
Mohammad Pezeshki, Diane Bouchacourt, Mark Ibrahim, Nicolas Ballas, Pascal Vincent, David Lopez-Paz
2024Discovering Features with Synergistic Interactions in Multiple Views.
Chohee Kim, Mihaela van der Schaar, Changhee Lee
2024Discovering Mixtures of Structural Causal Models from Time Series Data.
Sumanth Varambally, Yian Ma, Rose Yu
2024Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning.
Takayuki Osa, Tatsuya Harada
2024Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution.
Rui Wang, Elyssa F. Hofgard, Hang Gao, Robin Walters, Tess E. Smidt
2024Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution.
Aaron Lou, Chenlin Meng, Stefano Ermon
2024Discrete Latent Perspective Learning for Segmentation and Detection.
Deyi Ji, Feng Zhao, Lanyun Zhu, Wenwei Jin, Hongtao Lu, Jieping Ye
2024Disentangled 3D Scene Generation with Layout Learning.
Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A. Efros, Aleksander Holynski
2024Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet.
Zeyang Zhang, Xin Wang, Yijian Qin, Hong Chen, Ziwei Zhang, Xu Chu, Wenwu Zhu
2024Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization.
Haoyang Li, Xin Wang, Zeyang Zhang, Haibo Chen, Ziwei Zhang, Wenwu Zhu
2024Disentanglement Learning via Topology.
Nikita Balabin, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov
2024Disguised Copyright Infringement of Latent Diffusion Models.
Yiwei Lu, Matthew Y. R. Yang, Zuoqiu Liu, Gautam Kamath, Yaoliang Yu
2024Disparate Impact on Group Accuracy of Linearization for Private Inference.
Saswat Das, Marco Romanelli, Ferdinando Fioretto
2024Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion.
Ishaan Singh Rawal, Alexander Matyasko, Shantanu Jaiswal, Basura Fernando, Cheston Tan
2024DistiLLM: Towards Streamlined Distillation for Large Language Models.
Jongwoo Ko, Sungnyun Kim, Tianyi Chen, Se-Young Yun
2024Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control.
Zheng Xiong, Risto Vuorio, Jacob Beck, Matthieu Zimmer, Kun Shao, Shimon Whiteson
2024Distinguishing the Knowable from the Unknowable with Language Models.
Gustaf Ahdritz, Tian Qin, Nikhil Vyas, Boaz Barak, Benjamin L. Edelman
2024Distributed Bilevel Optimization with Communication Compression.
Yutong He, Jie Hu, Xinmeng Huang, Songtao Lu, Bin Wang, Kun Yuan
2024Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery.
Caixing Wang, Ziliang Shen
2024Distribution Alignment Optimization through Neural Collapse for Long-tailed Classification.
Jintong Gao, He Zhao, Dandan Guo, Hongyuan Zha
2024Distributional Bellman Operators over Mean Embeddings.
Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
2024Distributionally Robust Data Valuation.
Xiaoqiang Lin, Xinyi Xu, Zhaoxuan Wu, See-Kiong Ng, Bryan Kian Hsiang Low
2024Ditto: Quantization-aware Secure Inference of Transformers upon MPC.
Haoqi Wu, Wenjing Fang, Yancheng Zheng, Junming Ma, Jin Tan, Lei Wang
2024Diversified Batch Selection for Training Acceleration.
Feng Hong, Yueming Lyu, Jiangchao Yao, Ya Zhang, Ivor W. Tsang, Yanfeng Wang
2024Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset.
Shijie Lian, Ziyi Zhang, Hua Li, Wenjie Li, Laurence Tianruo Yang, Sam Kwong, Runmin Cong
2024Do Efficient Transformers Really Save Computation?
Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, Liwei Wang
2024Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
Andreas Opedal, Alessandro Stolfo, Haruki Shirakami, Ying Jiao, Ryan Cotterell, Bernhard Schölkopf, Abulhair Saparov, Mrinmaya Sachan
2024Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates.
Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis, Aaron Wilson, Kassem Fawaz, Somesh Jha
2024Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function.
Keyon Vafa, Ashesh Rambachan, Sendhil Mullainathan
2024Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations.
Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown
2024Do Topological Characteristics Help in Knowledge Distillation?
Jungeun Kim, Junwon You, Dongjin Lee, Ha Young Kim, Jae-Hun Jung
2024Do Transformer World Models Give Better Policy Gradients?
Michel Ma, Tianwei Ni, Clement Gehring, Pierluca D'Oro, Pierre-Luc Bacon
2024DoRA: Weight-Decomposed Low-Rank Adaptation.
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, Min-Hung Chen
2024Does Label Smoothing Help Deep Partial Label Learning?
Xiuwen Gong, Nitin Bisht, Guandong Xu
2024Domain Generalisation via Imprecise Learning.
Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet
2024Domain-wise Data Acquisition to Improve Performance under Distribution Shift.
Yue He, Dongbai Li, Pengfei Tian, Han Yu, Jiashuo Liu, Hao Zou, Peng Cui
2024Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget.
Florian E. Dorner, Moritz Hardt
2024Don't be so Negative! Score-based Generative Modeling with Oracle-assisted Guidance.
Saeid Naderiparizi, Xiaoxuan Liang, Setareh Cohan, Berend Zwartsenberg, Frank Wood
2024Don't trust your eyes: on the (un)reliability of feature visualizations.
Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim
2024DoraemonGPT: Toward Understanding Dynamic Scenes with Large Language Models (Exemplified as A Video Agent).
Zongxin Yang, Guikun Chen, Xiaodi Li, Wenguan Wang, Yi Yang
2024Double Momentum Method for Lower-Level Constrained Bilevel Optimization.
Wanli Shi, Yi Chang, Bin Gu
2024Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods.
Hao Di, Haishan Ye, Xiangyu Chang, Guang Dai, Ivor W. Tsang
2024Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient.
Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor W. Tsang
2024Double-Step Alternating Extragradient with Increasing Timescale Separation for Finding Local Minimax Points: Provable Improvements.
Kyuwon Kim, Donghwan Kim
2024Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning.
Weilin Chen, Ruichu Cai, Zeqin Yang, Jie Qiao, Yuguang Yan, Zijian Li, Zhifeng Hao
2024Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming.
Hany Hamed, Subin Kim, Dongyeong Kim, Jaesik Yoon, Sungjin Ahn
2024Drug Discovery with Dynamic Goal-aware Fragments.
Seul Lee, Seanie Lee, Kenji Kawaguchi, Sung Ju Hwang
2024DsDm: Model-Aware Dataset Selection with Datamodels.
Logan Engstrom, Axel Feldmann, Aleksander Madry
2024Dual Operating Modes of In-Context Learning.
Ziqian Lin, Kangwook Lee
2024DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems.
Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez
2024DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems.
Kaibo He, Chenhui Zuo, Chengtian Ma, Yanan Sui
2024Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization.
Sam Reifenstein, Timothée G. Leleu, Yoshihisa Yamamoto
2024Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers.
Ron Dorfman, Naseem Yehya, Kfir Yehuda Levy
2024Dynamic Correlation Clustering in Sublinear Update Time.
Vincent Cohen-Addad, Silvio Lattanzi, Andreas Maggiori, Nikos Parotsidis
2024Dynamic Evaluation of Large Language Models by Meta Probing Agents.
Kaijie Zhu, Jindong Wang, Qinlin Zhao, Ruochen Xu, Xing Xie
2024Dynamic Facility Location in High Dimensional Euclidean Spaces.
Sayan Bhattacharya, Gramoz Goranci, Shaofeng H.-C. Jiang, Yi Qian, Yubo Zhang
2024Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference.
Piotr Nawrot, Adrian Lancucki, Marcin Chochowski, David Tarjan, Edoardo M. Ponti
2024Dynamic Metric Embedding into lp Space.
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Dariusz Rafal Kowalski, Jan Olkowski, Max Springer
2024Dynamic Spectral Clustering with Provable Approximation Guarantee.
Steinar Laenen, He Sun
2024Dynamic Survival Analysis with Controlled Latent States.
Linus Bleistein, Van-Tuan Nguyen, Adeline Fermanian, Agathe Guilloux
2024DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving.
Foteini Strati, Sara McAllister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic
2024E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning.
Dingyang Chen, Qi Zhang
2024E2GAN: Efficient Training of Efficient GANs for Image-to-Image Translation.
Yifan Gong, Zheng Zhan, Qing Jin, Yanyu Li, Yerlan Idelbayev, Xian Liu, Andrey Zharkov, Kfir Aberman, Sergey Tulyakov, Yanzhi Wang, Jian Ren
2024EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty.
Yuhui Li, Fangyun Wei, Chao Zhang, Hongyang Zhang
2024ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance.
Liwen Sun, Abhineet Agarwal, Aaron Kornblith, Bin Yu, Chenyan Xiong
2024EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization.
Zhibin Gu, Zhendong Li, Songhe Feng
2024EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism.
Yanxi Chen, Xuchen Pan, Yaliang Li, Bolin Ding, Jingren Zhou
2024ELF: Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis.
Jungil Kong, Junmo Lee, Jeongmin Kim, Beomjeong Kim, Jihoon Park, Dohee Kong, Changheon Lee, Sangjin Kim
2024ELTA: An Enhancer against Long-Tail for Aesthetics-oriented Models.
Limin Liu, Shuai He, Anlong Ming, Rui Xie, Huadong Ma
2024EMC2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence.
Chung-Yiu Yau, Hoi-To Wai, Parameswaran Raman, Soumajyoti Sarkar, Mingyi Hong
2024ERQ: Error Reduction for Post-Training Quantization of Vision Transformers.
Yunshan Zhong, Jiawei Hu, You Huang, Yuxin Zhang, Rongrong Ji
2024ESM All-Atom: Multi-Scale Protein Language Model for Unified Molecular Modeling.
Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao Zhou
2024ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection.
Hongyu Liu, Runmin Cong, Hua Li, Qianqian Xu, Qingming Huang, Wei Zhang
2024ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections.
Massimo Bini, Karsten Roth, Zeynep Akata, Anna Khoreva
2024EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens.
Sunil Hwang, Jaehong Yoon, Youngwan Lee, Sung Ju Hwang
2024Early Time Classification with Accumulated Accuracy Gap Control.
Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano
2024Easing Concept Bleeding in Diffusion via Entity Localization and Anchoring.
Jiewei Zhang, Song Guo, Peiran Dong, Jie Zhang, Ziming Liu, Yue Yu, Xiao-Ming Wu
2024Editing Partially Observable Networks via Graph Diffusion Models.
Puja Trivedi, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra
2024Effective Federated Graph Matching.
Yang Zhou, Zijie Zhang, Zeru Zhang, Lingjuan Lyu, Wei-Shinn Ku
2024Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing.
Youwei Shu, Xi Xiao, Derui Wang, Yuxin Cao, Siji Chen, Jason Xue, Linyi Li, Bo Li
2024Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning.
Yizhe Huang, Anji Liu, Fanqi Kong, Yaodong Yang, Song-Chun Zhu, Xue Feng
2024Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond.
Dingzhi Yu, Yunuo Cai, Wei Jiang, Lijun Zhang
2024Efficient Algorithms for Sum-Of-Minimum Optimization.
Lisang Ding, Ziang Chen, Xinshang Wang, Wotao Yin
2024Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior.
Shuyu Cheng, Yibo Miao, Yinpeng Dong, Xiao Yang, Xiao-Shan Gao, Jun Zhu
2024Efficient Contextual Bandits with Uninformed Feedback Graphs.
Mengxiao Zhang, Yuheng Zhang, Haipeng Luo, Paul Mineiro
2024Efficient Contrastive Learning for Fast and Accurate Inference on Graphs.
Teng Xiao, Huaisheng Zhu, Zhiwei Zhang, Zhimeng Guo, Charu C. Aggarwal, Suhang Wang, Vasant G. Honavar
2024Efficient Denoising Diffusion via Probabilistic Masking.
Weizhong Zhang, Zhiwei Zhang, Renjie Pi, Zhongming Jin, Yuan Gao, Jieping Ye, Kani Chen
2024Efficient Error Certification for Physics-Informed Neural Networks.
Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy Dj Dvijotham, Philip Torr, M. Pawan Kumar
2024Efficient Exploration for LLMs.
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, Benjamin Van Roy
2024Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling.
Danil Provodin, Maurits Clemens Kaptein, Mykola Pechenizkiy
2024Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits.
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
2024Efficient Mixture Learning in Black-Box Variational Inference.
Alexandra Hotti, Oskar Kviman, Ricky Molén, Víctor Elvira, Jens Lagergren
2024Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation.
Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou
2024Efficient Online Set-valued Classification with Bandit Feedback.
Zhou Wang, Xingye Qiao
2024Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks.
Zirou Qiu, Abhijin Adiga, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard Edwin Stearns, Anil Kumar S. Vullikanti
2024Efficient Pareto Manifold Learning with Low-Rank Structure.
Weiyu Chen, James T. Kwok
2024Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design.
Shuze Daniel Liu, Shangtong Zhang
2024Efficient Precision and Recall Metrics for Assessing Generative Models using Hubness-aware Sampling.
Yuanbang Liang, Jing Wu, Yu-Kun Lai, Yipeng Qin
2024Efficient Stochastic Approximation of Minimax Excess Risk Optimization.
Lijun Zhang, Haomin Bai, Wei-Wei Tu, Ping Yang, Yao Hu
2024Efficient Value Iteration for s-rectangular Robust Markov Decision Processes.
Navdeep Kumar, Kaixin Wang, Kfir Yehuda Levy, Shie Mannor
2024Efficient World Models with Context-Aware Tokenization.
Vincent Micheli, Eloi Alonso, François Fleuret
2024Efficient and Effective Time-Series Forecasting with Spiking Neural Networks.
Changze Lv, Yansen Wang, Dongqi Han, Xiaoqing Zheng, Xuanjing Huang, Dongsheng Li
2024EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data.
Shengjie Wang, Shaohuai Liu, Weirui Ye, Jiacheng You, Yang Gao
2024EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time.
Shengyao Lu, Bang Liu, Keith G. Mills, Jiao He, Di Niu
2024Eluder-based Regret for Stochastic Contextual MDPs.
Orin Levy, Asaf B. Cassel, Alon Cohen, Yishay Mansour
2024Embarrassingly Parallel GFlowNets.
Tiago da Silva, Luiz Max Carvalho, Amauri H. Souza, Samuel Kaski, Diego Mesquita
2024Embodied CoT Distillation From LLM To Off-the-shelf Agents.
Wonje Choi, Woo Kyung Kim, Minjong Yoo, Honguk Woo
2024Emergence of In-Context Reinforcement Learning from Noise Distillation.
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin, Viacheslav Sinii, Sergey Kolesnikov
2024Emergent Equivariance in Deep Ensembles.
Jan E. Gerken, Pan Kessel
2024Emergent Representations of Program Semantics in Language Models Trained on Programs.
Charles Jin, Martin C. Rinard
2024Empowering Graph Invariance Learning with Deep Spurious Infomax.
Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang
2024Enabling Few-Shot Learning with PID Control: A Layer Adaptive Optimizer.
Le Yu, Xinde Li, Pengfei Zhang, Zhentong Zhang, Fir Dunkin
2024Enabling Uncertainty Estimation in Iterative Neural Networks.
Nikita Durasov, Doruk Öner, Jonathan Donier, Hieu Le, Pascal Fua
2024Encodings for Prediction-based Neural Architecture Search.
Yash Akhauri, Mohamed S. Abdelfattah
2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations.
Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li
2024Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic.
Nicolas Alder, Ralf Herbrich
2024Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning.
Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang, Ruifeng Chen, Zhilong Zhang, Xinwei Chen, Yang Yu
2024Energy-based Backdoor Defense without Task-Specific Samples and Model Retraining.
Yudong Gao, Honglong Chen, Peng Sun, Zhe Li, Junjian Li, Huajie Shao
2024Enforcing Constraints in RNA Secondary Structure Predictions: A Post-Processing Framework Based on the Assignment Problem.
Geewon Suh, Gyeongjo Hwang, Seokjun Kang, Doojin Baek, Mingeun Kang
2024Enhancing Adversarial Robustness in SNNs with Sparse Gradients.
Yujia Liu, Tong Bu, Jianhao Ding, Zecheng Hao, Tiejun Huang, Zhaofei Yu
2024Enhancing Class-Imbalanced Learning with Pre-Trained Guidance through Class-Conditional Knowledge Distillation.
Lan Li, Xin-Chun Li, Han-Jia Ye, De-Chuan Zhan
2024Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation.
Lincan Cai, Shuang Li, Wenxuan Ma, Jingxuan Kang, Binhui Xie, Zixun Sun, Chengwei Zhu
2024Enhancing Implicit Shape Generators Using Topological Regularizations.
Liyan Chen, Yan Zheng, Yang Li, Lohit Anirudh Jagarapu, Haoxiang Li, Hao Kang, Gang Hua, Qixing Huang
2024Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning.
Zheng Huang, Qihui Yang, Dawei Zhou, Yujun Yan
2024Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models.
Zixin Zhang, Fan Qi, Changsheng Xu
2024Enhancing Sufficient Dimension Reduction via Hellinger Correlation.
Seungbeom Hong, Ilmun Kim, Jun Song
2024Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction.
Pranav Singh Chib, Pravendra Singh
2024Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning.
Yun-Hsuan Lien, Ping-Chun Hsieh, Tzu-Mao Li, Yu-Shuen Wang
2024Enhancing Vision Transformer: Amplifying Non-Linearity in Feedforward Network Module.
Yixing Xu, Chao Li, Dong Li, Xiao Sheng, Fan Jiang, Lu Tian, Ashish Sirasao, Emad Barsoum
2024Ensemble Pruning for Out-of-distribution Generalization.
Fengchun Qiao, Xi Peng
2024Entropy-Reinforced Planning with Large Language Models for Drug Discovery.
Xuefeng Liu, Chih-chan Tien, Peng Ding, Songhao Jiang, Rick L. Stevens
2024Environment Design for Inverse Reinforcement Learning.
Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis
2024Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection.
Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han
2024EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning.
Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho, Junmo Kim, Joon Son Chung
2024EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction.
Yang Zhang, Zhewei Wei, Ye Yuan, Chongxuan Li, Wenbing Huang
2024Equilibrium of Data Markets with Externality.
Safwan Hossain, Yiling Chen
2024Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency.
Yuchao Lin, Jacob Helwig, Shurui Gui, Shuiwang Ji
2024Equivariant Deep Weight Space Alignment.
Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik, Nadav Dym, Haggai Maron
2024Equivariant Diffusion for Crystal Structure Prediction.
Peijia Lin, Pin Chen, Rui Jiao, Qing Mo, Jianhuan Cen, Wenbing Huang, Yang Liu, Dan Huang, Yutong Lu
2024Equivariant Frames and the Impossibility of Continuous Canonicalization.
Nadav Dym, Hannah Lawrence, Jonathan W. Siegel
2024Equivariant Graph Neural Operator for Modeling 3D Dynamics.
Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar
2024Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning.
Kai Gan, Tong Wei
2024Error Feedback Can Accurately Compress Preconditioners.
Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh
2024Estimating Barycenters of Distributions with Neural Optimal Transport.
Alexander Kolesov, Petr Mokrov, Igor Udovichenko, Milena Gazdieva, Gudmund Pammer, Evgeny Burnaev, Alexander Korotin
2024Estimating Canopy Height at Scale.
Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke
2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction.
Undral Byambadalai, Tatsushi Oka, Shota Yasui
2024Estimating Unknown Population Sizes Using the Hypergeometric Distribution.
Liam Hodgson, Danilo Bzdok
2024Estimating the Permanent by Nesting Importance Sampling.
Juha Harviainen, Mikko Koivisto
2024Et Tu Certifications: Robustness Certificates Yield Better Adversarial Examples.
Andrew C. Cullen, Shijie Liu, Paul Montague, Sarah Monazam Erfani, Benjamin I. P. Rubinstein
2024Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems.
David T. Hoffmann, Simon Schrodi, Jelena Bratulic, Nadine Behrmann, Volker Fischer, Thomas Brox
2024EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting.
Jiaxu Wang, Junhao He, Ziyi Zhang, Mingyuan Sun, Jingkai Sun, Renjing Xu
2024EvIL: Evolution Strategies for Generalisable Imitation Learning.
Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster
2024EvTexture: Event-driven Texture Enhancement for Video Super-Resolution.
Dachun Kai, Jiayao Lu, Yueyi Zhang, Xiaoyan Sun
2024Evaluating Model Bias Requires Characterizing its Mistakes.
Isabela Albuquerque, Jessica Schrouff, David Warde-Farley, Ali Taylan Cemgil, Sven Gowal, Olivia Wiles
2024Evaluating Quantized Large Language Models.
Shiyao Li, Xuefei Ning, Luning Wang, Tengxuan Liu, Xiangsheng Shi, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang
2024Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models.
Mingrui Wu, Jiayi Ji, Oucheng Huang, Jiale Li, Yuhang Wu, Xiaoshuai Sun, Rongrong Ji
2024Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks.
Linyuan Gong, Sida Wang, Mostafa Elhoushi, Alvin Cheung
2024Evaluation of Test-Time Adaptation Under Computational Time Constraints.
Motasem Alfarra, Hani Itani, Alejandro Pardo, Shyma Alhuwaider, Merey Ramazanova, Juan Camilo Pérez, Zhipeng Cai, Matthias Müller, Bernard Ghanem
2024Evaluation of Trajectory Distribution Predictions with Energy Score.
Novin Shahroudi, Mihkel Lepson, Meelis Kull
2024EvoRainbow: Combining Improvements in Evolutionary Reinforcement Learning for Policy Search.
Pengyi Li, Yan Zheng, Hongyao Tang, Xian Fu, Jianye Hao
2024EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs.
Haohui Wang, Yuzhen Mao, Yujun Yan, Yaoqing Yang, Jianhui Sun, Kevin Choi, Balaji Veeramani, Alison Hu, Edward Bowen, Tyler Cody, Dawei Zhou
2024Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model.
Fei Liu, Xialiang Tong, Mingxuan Yuan, Xi Lin, Fu Luo, Zhenkun Wang, Zhichao Lu, Qingfu Zhang
2024Evolution-Inspired Loss Functions for Protein Representation Learning.
Chengyue Gong, Adam R. Klivans, James Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz
2024Evolving Subnetwork Training for Large Language Models.
Hanqi Li, Lu Chen, Da Ma, Zijian Wu, Su Zhu, Kai Yu
2024ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking.
Wenshuo Li, Xinghao Chen, Han Shu, Yehui Tang, Yunhe Wang
2024Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers.
Brian K. Chen, Tianyang Hu, Hui Jin, Hwee Kuan Lee, Kenji Kawaguchi
2024Exact Soft Analytical Side-Channel Attacks using Tractable Circuits.
Thomas Wedenig, Rishub Nagpal, Gaëtan Cassiers, Stefan Mangard, Robert Peharz
2024Executable Code Actions Elicit Better LLM Agents.
Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji
2024Expand-and-Cluster: Parameter Recovery of Neural Networks.
Flavio Martinelli, Berfin Simsek, Wulfram Gerstner, Johanni Brea
2024Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning.
Chia-Cheng Chiang, Li-Cheng Lan, Wei-Fang Sun, Chien Feng, Cho-Jui Hsieh, Chun-Yi Lee
2024Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs.
Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison
2024Explain Temporal Black-Box Models via Functional Decomposition.
Linxiao Yang, Yunze Tong, Xinyue Gu, Liang Sun
2024Explaining Graph Neural Networks via Structure-aware Interaction Index.
Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying
2024Explaining Probabilistic Models with Distributional Values.
Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
2024Exploiting Code Symmetries for Learning Program Semantics.
Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana
2024Exploiting Human-AI Dependence for Learning to Defer.
Zixi Wei, Yuzhou Cao, Lei Feng
2024Exploiting Negative Samples: A Catalyst for Cohort Discovery in Healthcare Analytics.
Kaiping Zheng, Horng Ruey Chua, Melanie Herschel, H. V. Jagadish, Beng Chin Ooi, James Wei Luen Yip
2024Exploration and Anti-Exploration with Distributional Random Network Distillation.
Kai Yang, Jian Tao, Jiafei Lyu, Xiu Li
2024Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring.
Taira Tsuchiya, Shinji Ito, Junya Honda
2024Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization.
Yihan Du, Anna Winnicki, Gal Dalal, Shie Mannor, R. Srikant
2024Explorations of Self-Repair in Language Models.
Cody Rushing, Neel Nanda
2024Exploring Correlations of Self-Supervised Tasks for Graphs.
Taoran Fang, Wei Chow, Yifei Sun, Kaiqiao Han, Lvbin Ma, Yang Yang
2024Exploring Intrinsic Dimension for Vision-Language Model Pruning.
Hanzhang Wang, Jiawen Zhang, Qingyuan Ma
2024Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters.
Yuhang Zhou, Zihua Zhao, Siyuan Du, Haolin Li, Jiangchao Yao, Ya Zhang, Yanfeng Wang
2024Exploring the Benefit of Activation Sparsity in Pre-training.
Zhengyan Zhang, Chaojun Xiao, Qiujieli Qin, Yankai Lin, Zhiyuan Zeng, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou
2024Exploring the Complexity of Deep Neural Networks through Functional Equivalence.
Guohao Shen
2024Exploring the Enigma of Neural Dynamics Through A Scattering-Transform Mixer Landscape for Riemannian Manifold.
Tingting Dan, Ziquan Wei, Won Hwa Kim, Guorong Wu
2024Exploring the LLM Journey from Cognition to Expression with Linear Representations.
Yuzi Yan, Jialian Li, Yipin Zhang, Dong Yan
2024Exploring the Low-Pass Filtering Behavior in Image Super-Resolution.
Haoyu Deng, Zijing Xu, Yule Duan, Xiao Wu, Wenjie Shu, Liang-Jian Deng
2024Exponential Spectral Pursuit: An Effective Initialization Method for Sparse Phase Retrieval.
Mengchu Xu, Yuxuan Zhang, Jian Wang
2024Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann
2024Extending Test-Time Augmentation with Metamorphic Relations for Combinatorial Problems.
Siwei Wei, Xudong Zhang, Zhiyang Zhou, Yan Cai
2024Extracting Training Data From Document-Based VQA Models.
Francesco Pinto, Nathalie Rauschmayr, Florian Tramèr, Philip Torr, Federico Tombari
2024Extreme Compression of Large Language Models via Additive Quantization.
Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh
2024FADAS: Towards Federated Adaptive Asynchronous Optimization.
Yujia Wang, Shiqiang Wang, Songtao Lu, Jinghui Chen
2024FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames.
Ruidong Wu, Ruihan Guo, Rui Wang, Shitong Luo, Yue Xu, Jiahan Li, Jianzhu Ma, Qiang Liu, Yunan Luo, Jian Peng
2024FESSNC: Fast Exponentially Stable and Safe Neural Controller.
Jingdong Zhang, Luan Yang, Qunxi Zhu, Wei Lin
2024FRAG: Frequency Adapting Group for Diffusion Video Editing.
Sunjae Yoon, Gwanhyeong Koo, Geonwoo Kim, Chang D. Yoo
2024FRAPPÉ: A Group Fairness Framework for Post-Processing Everything.
Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost
2024Factored-Reward Bandits with Intermediate Observations.
Marco Mussi, Simone Drago, Marcello Restelli, Alberto Maria Metelli
2024Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models.
Som Sagar, Aditya Taparia, Ransalu Senanayake
2024Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach.
Vijay Keswani, Anay Mehrotra, L. Elisa Celis
2024Fair Federated Learning via the Proportional Veto Core.
Bhaskar Ray Chaudhury, Aniket Murhekar, Zhuowen Yuan, Bo Li, Ruta Mehta, Ariel D. Procaccia
2024Fair Off-Policy Learning from Observational Data.
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
2024Fair Resource Allocation in Multi-Task Learning.
Hao Ban, Kaiyi Ji
2024Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks.
Lujing Zhang, Aaron Roth, Linjun Zhang
2024FairProof : Confidential and Certifiable Fairness for Neural Networks.
Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
2024Faithfulness Measurable Masked Language Models.
Andreas Madsen, Siva Reddy, Sarath Chandar
2024Fast Adversarial Attacks on Language Models In One GPU Minute.
Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Malemir Chegini, Soheil Feizi
2024Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning.
Konstantinos Ameranis, Adela Frances DePavia, Lorenzo Orecchia, Erasmo Tani
2024Fast Co-Training under Weak Dependence via Stream-Based Active Learning.
Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos
2024Fast Decision Boundary based Out-of-Distribution Detector.
Litian Liu, Yao Qin
2024Fast Peer Adaptation with Context-aware Exploration.
Long Ma, Yuanfei Wang, Fangwei Zhong, Song-Chun Zhu, Yizhou Wang
2024Fast Sampling-Based Sketches for Tensors.
William J. Swartworth, David P. Woodruff
2024Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization.
Jinlu Zhang, Yiyi Zhou, Qiancheng Zheng, Xiaoxiong Du, Gen Luo, Jun Peng, Xiaoshuai Sun, Rongrong Ji
2024Fast Timing-Conditioned Latent Audio Diffusion.
Zach Evans, CJ Carr, Josiah Taylor, Scott H. Hawley, Jordi Pons
2024Fast White-Box Adversarial Streaming Without a Random Oracle.
Ying Feng, Aayush Jain, David P. Woodruff
2024Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits.
Jiabin Lin, Shana Moothedath, Namrata Vaswani
2024Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching.
Rico Angell, Andrew McCallum
2024Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation.
Junyu Gao, Xuan Yao, Changsheng Xu
2024Faster Adaptive Decentralized Learning Algorithms.
Feihu Huang, Jianyu Zhao
2024Faster Maximum Inner Product Search in High Dimensions.
Mo Tiwari, Ryan Kang, Jaeyong Lee, Donghyun Lee, Christopher Piech, Sebastian Thrun, Ilan Shomorony, Martin Jinye Zhang
2024Faster Sampling via Stochastic Gradient Proximal Sampler.
Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang
2024Faster Streaming and Scalable Algorithms for Finding Directed Dense Subgraphs in Large Graphs.
Slobodan Mitrovic, Theodore Pan
2024Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training.
Tehila Dahan, Kfir Yehuda Levy
2024Feasibility Consistent Representation Learning for Safe Reinforcement Learning.
Zhepeng Cen, Yihang Yao, Zuxin Liu, Ding Zhao
2024Feasible Reachable Policy Iteration.
Shentao Qin, Yujie Yang, Yao Mu, Jie Li, Wenjun Zou, Jingliang Duan, Shengbo Eben Li
2024Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation.
Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, José Miguel Hernández-Lobato
2024Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize.
Tianren Zhang, Chujie Zhao, Guanyu Chen, Yizhou Jiang, Feng Chen
2024Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective.
Soo Yong Lee, Sunwoo Kim, Fanchen Bu, Jaemin Yoo, Jiliang Tang, Kijung Shin
2024Feature Importance Disparities for Data Bias Investigations.
Peter W. Chang, Leor Fishman, Seth Neel
2024Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models.
Francesca-Zhoufan Li, Ava P. Amini, Yisong Yue, Kevin K. Yang, Alex Xijie Lu
2024FedBAT: Communication-Efficient Federated Learning via Learnable Binarization.
Shiwei Li, Wenchao Xu, Haozhao Wang, Xing Tang, Yining Qi, Shijie Xu, Weihong Luo, Yuhua Li, Xiuqiang He, Ruixuan Li
2024FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models.
Jingwei Sun, Ziyue Xu, Hongxu Yin, Dong Yang, Daguang Xu, Yudong Liu, Zhixu Du, Yiran Chen, Holger R. Roth
2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler.
Hongyi Peng, Han Yu, Xiaoli Tang, Xiaoxiao Li
2024FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees.
Jiahao Liu, Yipeng Zhou, Di Wu, Miao Hu, Mohsen Guizani, Quan Z. Sheng
2024FedMBridge: Bridgeable Multimodal Federated Learning.
Jiayi Chen, Aidong Zhang
2024FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering.
Yongxin Guo, Xiaoying Tang, Tao Lin
2024FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error.
Yueqi Xie, Minghong Fang, Neil Zhenqiang Gong
2024FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data.
Shusen Jing, Anlan Yu, Shuai Zhang, Songyang Zhang
2024Federated Combinatorial Multi-Agent Multi-Armed Bandits.
Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
2024Federated Continual Learning via Prompt-based Dual Knowledge Transfer.
Hongming Piao, Yichen Wu, Dapeng Wu, Ying Wei
2024Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes.
Zhen Qin, Daoyuan Chen, Bingchen Qian, Bolin Ding, Yaliang Li, Shuiguang Deng
2024Federated Neuro-Symbolic Learning.
Pengwei Xing, Songtao Lu, Han Yu
2024Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices.
Jiin Woo, Laixi Shi, Gauri Joshi, Yuejie Chi
2024Federated Optimization with Doubly Regularized Drift Correction.
Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2024Federated Representation Learning in the Under-Parameterized Regime.
Renpu Liu, Cong Shen, Jing Yang
2024Federated Self-Explaining GNNs with Anti-shortcut Augmentations.
Linan Yue, Qi Liu, Weibo Gao, Ye Liu, Kai Zhang, Yichao Du, Li Wang, Fangzhou Yao
2024Feedback Efficient Online Fine-Tuning of Diffusion Models.
Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Sergey Levine, Tommaso Biancalani
2024Feedback Loops With Language Models Drive In-Context Reward Hacking.
Alexander Pan, Erik Jones, Meena Jagadeesan, Jacob Steinhardt
2024Feel-Good Thompson Sampling for Contextual Dueling Bandits.
Xuheng Li, Heyang Zhao, Quanquan Gu
2024Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind.
Mo Yu, Qiujing Wang, Shunchi Zhang, Yisi Sang, Kangsheng Pu, Zekai Wei, Han Wang, Liyan Xu, Jing Li, Yue Yu, Jie Zhou
2024Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries.
Amine Ouasfi, Adnane Boukhayma
2024Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings.
Yihao Xue, Ali Payani, Yu Yang, Baharan Mirzasoleiman
2024Fewer Truncations Improve Language Modeling.
Hantian Ding, Zijian Wang, Giovanni Paolini, Varun Kumar, Anoop Deoras, Dan Roth, Stefano Soatto
2024FiT: Flexible Vision Transformer for Diffusion Model.
Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu, Xihui Liu, Wanli Ouyang, Lei Bai
2024FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning.
Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin
2024Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks.
Bjørn Leth Møller, Christian Igel, Kristoffer Knutsen Wickstrøm, Jon Sporring, Robert Jenssen, Bulat Ibragimov
2024Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning.
Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee
2024Fine-grained Classes and How to Find Them.
Matej Grcic, Artyom Gadetsky, Maria Brbic
2024Fine-grained Local Sensitivity Analysis of Standard Dot-Product Self-Attention.
Aaron J. Havens, Alexandre Araujo, Huan Zhang, Bin Hu
2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem.
Maciej Wolczyk, Bartlomiej Cupial, Mateusz Ostaszewski, Michal Bortkiewicz, Michal Zajac, Razvan Pascanu, Lukasz Kucinski, Piotr Milos
2024Finite Smoothing Algorithm for High-Dimensional Support Vector Machines and Quantile Regression.
Qian Tang, Yikai Zhang, Boxiang Wang
2024Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation.
Rahul Singh, Akshay Mete, Avik Kar, Panganamala R. Kumar
2024Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation.
Loh Sher En Jessica, Naheed Anjum Arafat, Wei Xian Lim, Wai Lee Chan, Adams Wai-Kin Kong
2024Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning.
Tianchen Zhou, Hairi, Haibo Yang, Jia Liu, Tian Tong, Fan Yang, Michinari Momma, Yan Gao
2024First-Order Manifold Data Augmentation for Regression Learning.
Ilya Kaufman, Omri Azencot
2024FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction.
Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang
2024Flexible Residual Binarization for Image Super-Resolution.
Yulun Zhang, Haotong Qin, Zixiang Zhao, Xianglong Liu, Martin Danelljan, Fisher Yu
2024Flextron: Many-in-One Flexible Large Language Model.
Ruisi Cai, Saurav Muralidharan, Greg Heinrich, Hongxu Yin, Zhangyang Wang, Jan Kautz, Pavlo Molchanov
2024Floating Anchor Diffusion Model for Multi-motif Scaffolding.
Ke Liu, Weian Mao, Shuaike Shen, Xiaoran Jiao, Zheng Sun, Hao Cheng, Chunhua Shen
2024Flora: Low-Rank Adapters Are Secretly Gradient Compressors.
Yongchang Hao, Yanshuai Cao, Lili Mou
2024FlowMM: Generating Materials with Riemannian Flow Matching.
Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M. Wood
2024Fool Your (Vision and) Language Model with Embarrassingly Simple Permutations.
Yongshuo Zong, Tingyang Yu, Ruchika Chavhan, Bingchen Zhao, Timothy M. Hospedales
2024Forget Sharpness: Perturbed Forgetting of Model Biases Within SAM Dynamics.
Ankit Vani, Frederick Tung, Gabriel L. Oliveira, Hossein Sharifi-Noghabi
2024Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024
Ruslan Salakhutdinov, Zico Kolter, Katherine A. Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp
2024Foundation Policies with Hilbert Representations.
Seohong Park, Tobias Kreiman, Sergey Levine
2024Foundations of Testing for Finite-Sample Causal Discovery.
Tom Yan, Ziyu Xu, Zachary Chase Lipton
2024Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning.
Hengkai Tan, Songming Liu, Kai Ma, Chengyang Ying, Xingxing Zhang, Hang Su, Jun Zhu
2024FrameQuant: Flexible Low-Bit Quantization for Transformers.
Harshavardhan Adepu, Zhanpeng Zeng, Li Zhang, Vikas Singh
2024FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion.
Zehan Wang, Ziang Zhang, Xize Cheng, Rongjie Huang, Luping Liu, Zhenhui Ye, Haifeng Huang, Yang Zhao, Tao Jin, Peng Gao, Zhou Zhao
2024From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions.
Trenton Chang, Jenna Wiens
2024From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble.
Qianlong Wen, Mingxuan Ju, Zhongyu Ouyang, Chuxu Zhang, Yanfang Ye
2024From Fourier to Neural ODEs: Flow Matching for Modeling Complex Systems.
Xin Li, Jingdong Zhang, Qunxi Zhu, Chengli Zhao, Xue Zhang, Xiaojun Duan, Wei Lin
2024From Generalization Analysis to Optimization Designs for State Space Models.
Fusheng Liu, Qianxiao Li
2024From Geometry to Causality- Ricci Curvature and the Reliability of Causal Inference on Networks.
Amirhossein Farzam, Allen R. Tannenbaum, Guillermo Sapiro
2024From Inverse Optimization to Feasibility to ERM.
Saurabh Mishra, Anant Raj, Sharan Vaswani
2024From Neurons to Neutrons: A Case Study in Interpretability.
Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz, Sokratis Trifinopoulos, Mike Williams
2024From Self-Attention to Markov Models: Unveiling the Dynamics of Generative Transformers.
Muhammed Emrullah Ildiz, Yixiao Huang, Yingcong Li, Ankit Singh Rawat, Samet Oymak
2024From Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation.
Kun Su, Xiulong Liu, Eli Shlizerman
2024From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems.
Jianliang He, Siyu Chen, Fengzhuo Zhang, Zhuoran Yang
2024From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning.
Wei Chen, Zhen Huang, Liang Xie, Binbin Lin, Houqiang Li, Le Lu, Xinmei Tian, Deng Cai, Yonggang Zhang, Wenxiao Wang, Xu Shen, Jieping Ye
2024FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning.
Yuwei Fu, Haichao Zhang, Di Wu, Wei Xu, Benoit Boulet
2024Full-Atom Peptide Design based on Multi-modal Flow Matching.
Jiahan Li, Chaoran Cheng, Zuofan Wu, Ruihan Guo, Shitong Luo, Zhizhou Ren, Jian Peng, Jianzhu Ma
2024Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees.
Marco Bressan, Mauro Sozio
2024Fundamental Benefit of Alternating Updates in Minimax Optimization.
Jaewook Lee, Hanseul Cho, Chulhee Yun
2024Fundamental Limitations of Alignment in Large Language Models.
Yotam Wolf, Noam Wies, Oshri Avnery, Yoav Levine, Amnon Shashua
2024Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints.
Mohammad-Reza Rahmani, Mohammad Hossein Yassaee, Mohammad Ali Maddah-Ali, Mohammad Reza Aref
2024GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting.
Xiaoyu Zhou, Xingjian Ran, Yajiao Xiong, Jinlin He, Zhiwei Lin, Yongtao Wang, Deqing Sun, Ming-Hsuan Yang
2024GATE: How to Keep Out Intrusive Neighbors.
Nimrah Mustafa, Rebekka Burkholz
2024GFlowNet Training by Policy Gradients.
Puhua Niu, Shili Wu, Mingzhou Fan, Xiaoning Qian
2024GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements.
Alexander Havrilla, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, Roberta Raileanu
2024GNNs Also Deserve Editing, and They Need It More Than Once.
Shaochen (Henry) Zhong, Duy Le, Zirui Liu, Zhimeng Jiang, Andrew Ye, Jiamu Zhang, Jiayi Yuan, Kaixiong Zhou, Zhaozhuo Xu, Jing Ma, Shuai Xu, Vipin Chaudhary, Xia Hu
2024GPT-4V(ision) is a Generalist Web Agent, if Grounded.
Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, Yu Su
2024GPTSwarm: Language Agents as Optimizable Graphs.
Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber
2024GRATH: Gradual Self-Truthifying for Large Language Models.
Weixin Chen, Dawn Song, Bo Li
2024GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.
Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian
2024Gambling-Based Confidence Sequences for Bounded Random Vectors.
Jongha Jon Ryu, Gregory W. Wornell
2024Gated Linear Attention Transformers with Hardware-Efficient Training.
Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, Yoon Kim
2024Gaussian Plane-Wave Neural Operator for Electron Density Estimation.
Seongsu Kim, Sungsoo Ahn
2024Gaussian Processes on Cellular Complexes.
Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth
2024GaussianPro: 3D Gaussian Splatting with Progressive Propagation.
Kai Cheng, Xiaoxiao Long, Kaizhi Yang, Yao Yao, Wei Yin, Yuexin Ma, Wenping Wang, Xuejin Chen
2024GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer.
Ding Jia, Jianyuan Guo, Kai Han, Han Wu, Chao Zhang, Chang Xu, Xinghao Chen
2024GenCO: Generating Diverse Designs with Combinatorial Constraints.
Aaron M. Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian
2024Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning.
Xiangzhe Kong, Wenbing Huang, Yang Liu
2024Generalization Analysis for Multi-Label Learning.
Yifan Zhang, Min-Ling Zhang
2024Generalization Analysis of Deep Non-linear Matrix Completion.
Antoine Ledent, Rodrigo Alves
2024Generalization Analysis of Stochastic Weight Averaging with General Sampling.
Peng Wang, Li Shen, Zerui Tao, Shuaida He, Dacheng Tao
2024Generalization Bound and New Algorithm for Clean-Label Backdoor Attack.
Lijia Yu, Shuang Liu, Yibo Miao, Xiao-Shan Gao, Lijun Zhang
2024Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis.
Daniel Csillag, Cláudio José Struchiner, Guilherme Tegoni Goedert
2024Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation.
Benjamin Dupuis, Umut Simsekli
2024Generalization Error of Graph Neural Networks in the Mean-field Regime.
Gholamali Aminian, Yixuan He, Gesine Reinert, Lukasz Szpruch, Samuel N. Cohen
2024Generalization in Kernel Regression Under Realistic Assumptions.
Daniel Barzilai, Ohad Shamir
2024Generalization to New Sequential Decision Making Tasks with In-Context Learning.
Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, Roberta Raileanu
2024Generalized Neural Collapse for a Large Number of Classes.
Jiachen Jiang, Jinxin Zhou, Peng Wang, Qing Qu, Dustin G. Mixon, Chong You, Zhihui Zhu
2024Generalized Preference Optimization: A Unified Approach to Offline Alignment.
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Harvey Richemond, Michal Valko, Bernardo Ávila Pires, Bilal Piot
2024Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes.
Daniil Vankov, Angelia Nedich, Lalitha Sankar
2024Generalized Sobolev Transport for Probability Measures on a Graph.
Tam Le, Truyen Nguyen, Kenji Fukumizu
2024Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization.
Rui Li, Chaozhuo Li, Yanming Shen, Zeyu Zhang, Xu Chen
2024Generalizing Orthogonalization for Models with Non-Linearities.
David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
2024Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought.
Zhen-Yu Zhang, Siwei Han, Huaxiu Yao, Gang Niu, Masashi Sugiyama
2024Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks.
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mondal, Hua Wei, Dongsheng Luo
2024Generative Active Learning for Long-tailed Instance Segmentation.
Muzhi Zhu, Chengxiang Fan, Hao Chen, Yang Liu, Weian Mao, Xiaogang Xu, Chunhua Shen
2024Generative Conditional Distributions by Neural (Entropic) Optimal Transport.
Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen, Viet Anh Nguyen
2024Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates.
Zhenqiao Song, Yunlong Zhao, Wenxian Shi, Wengong Jin, Yang Yang, Lei Li
2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design.
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, Tommi S. Jaakkola
2024Generative Marginalization Models.
Sulin Liu, Peter J. Ramadge, Ryan P. Adams
2024Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes.
Jaehyeong Jo, Sung Ju Hwang
2024Genie: Generative Interactive Environments.
Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel
2024GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation.
Haitao Lin, Lirong Wu, Yufei Huang, Yunfan Liu, Odin Zhang, Yuanqing Zhou, Rui Sun, Stan Z. Li
2024GeoMFormer: A General Architecture for Geometric Molecular Representation Learning.
Tianlang Chen, Shengjie Luo, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang
2024GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model.
Ling Li, Yu Ye, Bingchuan Jiang, Wei Zeng
2024Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction.
Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, Mirco Mutti, Andreas Krause
2024Geometry-Aware Instrumental Variable Regression.
Heiner Kremer, Bernhard Schölkopf
2024Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications.
Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui
2024Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference.
Harry Dong, Xinyu Yang, Zhenyu Zhang, Zhangyang Wang, Yuejie Chi, Beidi Chen
2024Getting the most out of your tokenizer for pre-training and domain adaptation.
Gautier Dagan, Gabriel Synnaeve, Baptiste Rozière
2024GiLOT: Interpreting Generative Language Models via Optimal Transport.
Xuhong Li, Jiamin Chen, Yekun Chai, Haoyi Xiong
2024Gibbs Sampling of Continuous Potentials on a Quantum Computer.
Arsalan Motamedi, Pooya Ronagh
2024GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks.
Shivanshu Gupta, Clemens Rosenbaum, Ethan R. Elenberg
2024GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding.
Cunxiao Du, Jing Jiang, Yuanchen Xu, Jiawei Wu, Sicheng Yu, Yongqi Li, Shenggui Li, Kai Xu, Liqiang Nie, Zhaopeng Tu, Yang You
2024Global Reinforcement Learning : Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods.
Riccardo De Santi, Manish Prajapat, Andreas Krause
2024Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations.
Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos, Volkan Cevher
2024Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization.
Ruizhong Qiu, Hanghang Tong
2024Gradient-based Visual Explanation for Transformer-based CLIP.
Chenyang Zhao, Kun Wang, Xingyu Zeng, Rui Zhao, Antoni B. Chan
2024Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method.
Kishaan Jeeveswaran, Elahe Arani, Bahram Zonooz
2024Graph Adversarial Diffusion Convolution.
Songtao Liu, Jinghui Chen, Tianfan Fu, Lu Lin, Marinka Zitnik, Dinghao Wu
2024Graph As Point Set.
Xiyuan Wang, Pan Li, Muhan Zhang
2024Graph Automorphism Group Equivariant Neural Networks.
Edward Pearce-Crump, William J. Knottenbelt
2024Graph Distillation with Eigenbasis Matching.
Yang Liu, Deyu Bo, Chuan Shi
2024Graph External Attention Enhanced Transformer.
Jianqing Liang, Min Chen, Jiye Liang
2024Graph Generation with Diffusion Mixture.
Jaehyeong Jo, Dongki Kim, Sung Ju Hwang
2024Graph Geometry-Preserving Autoencoders.
Jungbin Lim, Jihwan Kim, Yonghyeon Lee, Cheongjae Jang, Frank C. Park
2024Graph Mixup on Approximate Gromov-Wasserstein Geodesics.
Zhichen Zeng, Ruizhong Qiu, Zhe Xu, Zhining Liu, Yuchen Yan, Tianxin Wei, Lei Ying, Jingrui He, Hanghang Tong
2024Graph Neural Network Explanations are Fragile.
Jiate Li, Meng Pang, Yun Dong, Jinyuan Jia, Binghui Wang
2024Graph Neural Networks Use Graphs When They Shouldn't.
Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson
2024Graph Neural Networks with a Distribution of Parametrized Graphs.
See Hian Lee, Feng Ji, Kelin Xia, Wee Peng Tay
2024Graph Neural PDE Solvers with Conservation and Similarity-Equivariance.
Masanobu Horie, Naoto Mitsume
2024Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification.
Xixun Lin, Wenxiao Zhang, Fengzhao Shi, Chuan Zhou, Lixin Zou, Xiangyu Zhao, Dawei Yin, Shirui Pan, Yanan Cao
2024Graph Out-of-Distribution Detection Goes Neighborhood Shaping.
Tianyi Bao, Qitian Wu, Zetian Jiang, Yiting Chen, Jiawei Sun, Junchi Yan
2024Graph Positional and Structural Encoder.
Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampásek
2024Graph Structure Extrapolation for Out-of-Distribution Generalization.
Xiner Li, Shurui Gui, Youzhi Luo, Shuiwang Ji
2024Graph-Triggered Rising Bandits.
Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli
2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling.
Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi
2024Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting.
Andrea Cini, Danilo P. Mandic, Cesare Alippi
2024Graph-enhanced Large Language Models in Asynchronous Plan Reasoning.
Fangru Lin, Emanuele La Malfa, Valentin Hofmann, Elle Michelle Yang, Anthony G. Cohn, Janet B. Pierrehumbert
2024Graph2Tac: Online Representation Learning of Formal Math Concepts.
Lasse Blaauwbroek, Mirek Olsák, Jason Rute, Fidel Ivan Schaposnik Massolo, Jelle Piepenbrock, Vasily Pestun
2024Graphon Mean Field Games with a Representative Player: Analysis and Learning Algorithm.
Fuzhong Zhou, Chenyu Zhang, Xu Chen, Xuan Di
2024Grokking Group Multiplication with Cosets.
Dashiell Stander, Qinan Yu, Honglu Fan, Stella Biderman
2024GroupCover: A Secure, Efficient and Scalable Inference Framework for On-device Model Protection based on TEEs.
Zheng Zhang, Na Wang, Ziqi Zhang, Yao Zhang, Tianyi Zhang, Jianwei Liu, Ye Wu
2024Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples.
Thomas T. C. K. Zhang, Bruce D. Lee, Ingvar M. Ziemann, George J. Pappas, Nikolai Matni
2024Guidance with Spherical Gaussian Constraint for Conditional Diffusion.
Lingxiao Yang, Shutong Ding, Yifan Cai, Jingyi Yu, Jingya Wang, Ye Shi
2024Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation.
Luca Beurer-Kellner, Marc Fischer, Martin T. Vechev
2024H-Consistency Guarantees for Regression.
Anqi Mao, Mehryar Mohri, Yutao Zhong
2024HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding.
Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, Jiawei Zhou
2024HAMLET: Graph Transformer Neural Operator for Partial Differential Equations.
Andrey Bryutkin, Jiahao Huang, Zhongying Deng, Guang Yang, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero
2024HGAP: Boosting Permutation Invariant and Permutation Equivariant in Multi-Agent Reinforcement Learning via Graph Attention Network.
Bor-Jiun Lin, Chun-Yi Lee
2024HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming.
Yang Wu, Yifan Zhang, Zhenxing Liang, Jian Cheng
2024Handling Heterogeneous Curvatures in Bandit LQR Control.
Yu-Hu Yan, Jing Wang, Peng Zhao
2024Hard Tasks First: Multi-Task Reinforcement Learning Through Task Scheduling.
Myungsik Cho, Jongeui Park, Suyoung Lee, Youngchul Sung
2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal.
Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David A. Forsyth, Dan Hendrycks
2024HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning.
Shengchao Hu, Ziqing Fan, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao
2024Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design.
Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi S. Jaakkola
2024Harmonizing Generalization and Personalization in Federated Prompt Learning.
Tianyu Cui, Hongxia Li, Jingya Wang, Ye Shi
2024Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis.
Stefan Horoi, Albert Manuel Orozco Camacho, Eugene Belilovsky, Guy Wolf
2024HarmonyDream: Task Harmonization Inside World Models.
Haoyu Ma, Jialong Wu, Ningya Feng, Chenjun Xiao, Dong Li, Jianye Hao, Jianmin Wang, Mingsheng Long
2024Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition.
Zhiyong Yang, Qianqian Xu, Zitai Wang, Sicong Li, Boyu Han, Shilong Bao, Xiaochun Cao, Qingming Huang
2024Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning.
Depeng Li, Tianqi Wang, Junwei Chen, Wei Dai, Zhigang Zeng
2024Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws.
Ning Liu, Yiming Fan, Xianyi Zeng, Milan Klöwer, Lu Zhang, Yue Yu
2024HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction.
Lanxiang Xing, Haixu Wu, Yuezhou Ma, Jianmin Wang, Mingsheng Long
2024Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions.
Jingtan Wang, Xiaoqiang Lin, Rui Qiao, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2024HexGen: Generative Inference of Large Language Model over Heterogeneous Environment.
Youhe Jiang, Ran Yan, Xiaozhe Yao, Yang Zhou, Beidi Chen, Binhang Yuan
2024Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks.
Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann
2024Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity.
Marta Catalano, Hugo Lavenant
2024Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution.
Xihaier Luo, Xiaoning Qian, Byung-Jun Yoon
2024Hierarchical Novelty Detection via Fine-Grained Evidence Allocation.
Spandan Pyakurel, Qi Yu
2024Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling.
Raunaq M. Bhirangi, Chenyu Wang, Venkatesh Pattabiraman, Carmel Majidi, Abhinav Gupta, Tess Lee Hellebrekers, Lerrel Pinto
2024Hieros: Hierarchical Imagination on Structured State Space Sequence World Models.
Paul Mattes, Rainer Schlosser, Ralf Herbrich
2024High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling.
Yuxuan Yin, Yu Wang, Peng Li
2024High-Dimensional Geometric Streaming for Nearly Low Rank Data.
Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
2024High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization.
Yihang Chen, Fanghui Liu, Taiji Suzuki, Volkan Cevher
2024High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion.
Yu Dai, Junchen Shen, Zijie Zhai, Danlin Liu, Jingyang Chen, Yu Sun, Ping Li, Jie Zhang, Kai Zhang
2024High-Performance Temporal Reversible Spiking Neural Networks with O(L) Training Memory and O(1) Inference Cost.
Jiakui Hu, Man Yao, Xuerui Qiu, Yuhong Chou, Yuxuan Cai, Ning Qiao, Yonghong Tian, Bo Xu, Guoqi Li
2024High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails.
Langqi Liu, Yibo Wang, Lijun Zhang
2024High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik
2024High-dimensional Linear Bandits with Knapsacks.
Wanteng Ma, Dong Xia, Jiashuo Jiang
2024Highway Value Iteration Networks.
Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, Jürgen Schmidhuber
2024Homomorphism Counts for Graph Neural Networks: All About That Basis.
Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan, Matthias Lanzinger
2024How Deep Do We Need: Accelerating Training and Inference of Neural ODEs via Control Perspective.
Keyan Miao, Konstantinos Gatsis
2024How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model.
Umberto M. Tomasini, Matthieu Wyart
2024How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?
Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen
2024How Does Goal Relabeling Improve Sample Efficiency?
Sirui Zheng, Chenjia Bai, Zhuoran Yang, Zhaoran Wang
2024How Far Can Fairness Constraints Help Recover From Biased Data?
Mohit Sharma, Amit Deshpande
2024How Flawed Is ECE? An Analysis via Logit Smoothing.
Muthu Chidambaram, Holden Lee, Colin McSwiggen, Semon Rezchikov
2024How Free is Parameter-Free Stochastic Optimization?
Amit Attia, Tomer Koren
2024How Graph Neural Networks Learn: Lessons from Training Dynamics.
Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan
2024How Interpretable Are Interpretable Graph Neural Networks?
Yongqiang Chen, Yatao Bian, Bo Han, James Cheng
2024How Language Model Hallucinations Can Snowball.
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, Noah A. Smith
2024How Learning by Reconstruction Produces Uninformative Features For Perception.
Randall Balestriero, Yann LeCun
2024How Private are DP-SGD Implementations?
Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
2024How Smooth Is Attention?
Valérie Castin, Pierre Ablin, Gabriel Peyré
2024How Spurious Features are Memorized: Precise Analysis for Random and NTK Features.
Simone Bombari, Marco Mondelli
2024How Transformers Learn Causal Structure with Gradient Descent.
Eshaan Nichani, Alex Damian, Jason D. Lee
2024How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers.
Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry
2024How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing.
Keke Huang, Yu Guang Wang, Ming Li, Pietro Lio
2024How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis.
Federico Bianchi, Patrick John Chia, Mert Yüksekgönül, Jacopo Tagliabue, Dan Jurafsky, James Zou
2024How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths
2024How do Transformers Perform In-Context Autoregressive Learning ?
Michael Eli Sander, Raja Giryes, Taiji Suzuki, Mathieu Blondel, Gabriel Peyré
2024How to Escape Sharp Minima with Random Perturbations.
Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra
2024How to Explore with Belief: State Entropy Maximization in POMDPs.
Riccardo Zamboni, Duilio Cirino, Marcello Restelli, Mirco Mutti
2024How to Leverage Diverse Demonstrations in Offline Imitation Learning.
Sheng Yue, Jiani Liu, Xingyuan Hua, Ju Ren, Sen Lin, Junshan Zhang, Yaoxue Zhang
2024How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization.
Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright
2024How to Trace Latent Generative Model Generated Images without Artificial Watermark?
Zhenting Wang, Vikash Sehwag, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma
2024Human Alignment of Large Language Models through Online Preference Optimisation.
Daniele Calandriello, Zhaohan Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
2024Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
2024Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks.
Akshay K. Jagadish, Julian Coda-Forno, Mirko Thalmann, Eric Schulz, Marcel Binz
2024HumanTOMATO: Text-aligned Whole-body Motion Generation.
Shunlin Lu, Ling-Hao Chen, Ailing Zeng, Jing Lin, Ruimao Zhang, Lei Zhang, Heung-Yeung Shum
2024Hybrid Inverse Reinforcement Learning.
Juntao Ren, Gokul Swamy, Steven Wu, Drew Bagnell, Sanjiban Choudhury
2024Hybrid Neural Representations for Spherical Data.
Hyomin Kim, Yunhui Jang, Jaeho Lee, Sungsoo Ahn
2024Hybrid Reinforcement Learning from Offline Observation Alone.
Yuda Song, Drew Bagnell, Aarti Singh
2024Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response.
Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari, Emily B. Fox
2024HyperFields: Towards Zero-Shot Generation of NeRFs from Text.
Sudarshan Babu, Richard Liu, Avery Zhou, Michael Maire, Greg Shakhnarovich, Rana Hanocka
2024Hyperbolic Active Learning for Semantic Segmentation under Domain Shift.
Luca Franco, Paolo Mandica, Konstantinos Kallidromitis, Devin Guillory, Yu-Teng Li, Trevor Darrell, Fabio Galasso
2024Hyperbolic Geometric Latent Diffusion Model for Graph Generation.
Xingcheng Fu, Yisen Gao, Yuecen Wei, Qingyun Sun, Hao Peng, Jianxin Li, Xianxian Li
2024Hyperbolic Optimizer as a Dynamical System.
Nicolás Alvarado, Hans Löbel
2024Hypergraph-enhanced Dual Semi-supervised Graph Classification.
Wei Ju, Zhengyang Mao, Siyu Yi, Yifang Qin, Yiyang Gu, Zhiping Xiao, Yifan Wang, Xiao Luo, Ming Zhang
2024I/O Complexity of Attention, or How Optimal is FlashAttention?
Barna Saha, Christopher Ye
2024IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency.
Linshan Hou, Ruili Feng, Zhongyun Hua, Wei Luo, Leo Yu Zhang, Yiming Li
2024IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation.
Kai Li, Runxuan Yang, Fuchun Sun, Xiaolin Hu
2024ILILT: Implicit Learning of Inverse Lithography Technologies.
Haoyu Yang, Haoxing Ren
2024IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation.
Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos
2024IM-Unpack: Training and Inference with Arbitrarily Low Precision Integers.
Zhanpeng Zeng, Karthikeyan Sankaralingam, Vikas Singh
2024INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer.
Han Fang, Zhihao Song, Paul Weng, Yutong Ban
2024IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics.
Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy S. Vatolin
2024IW-GAE: Importance weighted group accuracy estimation for improved calibration and model selection in unsupervised domain adaptation.
Taejong Joo, Diego Klabjan
2024Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank.
Mouxiang Chen, Chenghao Liu, Zemin Liu, Zhuo Li, Jianling Sun
2024Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach.
Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser
2024Image Clustering with External Guidance.
Yunfan Li, Peng Hu, Dezhong Peng, Jiancheng Lv, Jianping Fan, Xi Peng
2024Image Fusion via Vision-Language Model.
Zixiang Zhao, Lilun Deng, Haowen Bai, Yukun Cui, Zhipeng Zhang, Yulun Zhang, Haotong Qin, Dongdong Chen, Jiangshe Zhang, Peng Wang, Luc Van Gool
2024Image Hijacks: Adversarial Images can Control Generative Models at Runtime.
Luke Bailey, Euan Ong, Stuart Russell, Scott Emmons
2024Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge.
Conghan Yue, Zhengwei Peng, Junlong Ma, Shiyan Du, Pengxu Wei, Dongyu Zhang
2024Imitation Learning from Purified Demonstrations.
Yunke Wang, Minjing Dong, Yukun Zhao, Bo Du, Chang Xu
2024Imitation Learning in Discounted Linear MDPs without exploration assumptions.
Luca Viano, Stratis Skoulakis, Volkan Cevher
2024Impact of Decentralized Learning on Player Utilities in Stackelberg Games.
Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins
2024Implicit Bias of AdamW: ℓ∞-Norm Constrained Optimization.
Shuo Xie, Zhiyuan Li
2024Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States.
Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen
2024Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD.
Yijun Wan, Melih Barsbey, Abdellatif Zaidi, Umut Simsekli
2024Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks.
Zachary Robertson, Sanmi Koyejo
2024Implicit Representations for Constrained Image Segmentation.
Jan Philipp Schneider, Mishal Fatima, Jovita Lukasik, Andreas Kolb, Margret Keuper, Michael Moeller
2024Implicit Representations via Operator Learning.
Sourav Pal, Harshavardhan Adepu, Clinton J. Wang, Polina Golland, Vikas Singh
2024Implicit meta-learning may lead language models to trust more reliable sources.
Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Kacper Mlodozeniec, Tegan Maharaj, David Krueger
2024Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy.
Bo Li, Wei Wang, Peng Ye
2024Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy.
Wei-Ning Chen, Berivan Isik, Peter Kairouz, Albert No, Sewoong Oh, Zheng Xu
2024Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements.
Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta
2024Improved Dimensionality Dependence for Zeroth-Order Optimisation over Cross-Polytopes.
Weijia Shao
2024Improved Generalization of Weight Space Networks via Augmentations.
Aviv Shamsian, Aviv Navon, David W. Zhang, Yan Zhang, Ethan Fetaya, Gal Chechik, Haggai Maron
2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials.
Jonathan Scott, Áine Cahill
2024Improved Operator Learning by Orthogonal Attention.
Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su
2024Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm.
Batiste Le Bars, Aurélien Bellet, Marc Tommasi, Kevin Scaman, Giovanni Neglia
2024Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training.
Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu
2024Improving Adversarial Energy-Based Model via Diffusion Process.
Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Søren Hauberg, Bo Li
2024Improving Antibody Humanness Prediction using Patent Data.
Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni
2024Improving Computational Complexity in Statistical Models with Local Curvature Information.
Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho
2024Improving Context Understanding in Multimodal Large Language Models via Multimodal Composition Learning.
Wei Li, Hehe Fan, Yongkang Wong, Yi Yang, Mohan S. Kankanhalli
2024Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance.
Xinyu Peng, Ziyang Zheng, Wenrui Dai, Nuoqian Xiao, Chenglin Li, Junni Zou, Hongkai Xiong
2024Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning.
Yuelin Zhang, Jiacheng Cen, Jiaqi Han, Zhiqiang Zhang, Jun Zhou, Wenbing Huang
2024Improving Factuality and Reasoning in Language Models through Multiagent Debate.
Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch
2024Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting.
Da Wang, Lin Li, Wei Wei, Qixian Yu, Jianye Hao, Jiye Liang
2024Improving Gradient-Guided Nested Sampling for Posterior Inference.
Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault Levasseur
2024Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference.
Yujin Han, Difan Zou
2024Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation.
Joonho Lee, Jae Oh Woo, Juree Seok, Parisa Hassanzadeh, Wooseok Jang, JuYoun Son, Sima Didari, Baruch Gutow, Heng Hao, Hankyu Moon, Wenjun Hu, Yeong-Dae Kwon, Taehee Lee, Seungjai Min
2024Improving Interpretation Faithfulness for Vision Transformers.
Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
2024Improving Neural Additive Models with Bayesian Principles.
Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
2024Improving Neural Logic Machines via Failure Reflection.
Zhiming Li, Yushi Cao, Yan Zheng, Xu Liu, Bozhi Wu, Tianlin Li, Xiufeng Xu, Junzhe Jiang, Yon Shin Teo, Shang-Wei Lin, Yang Liu
2024Improving Open-Ended Text Generation via Adaptive Decoding.
Wenhong Zhu, Hongkun Hao, Zhiwei He, Yiming Ai, Rui Wang
2024Improving Prototypical Visual Explanations with Reward Reweighing, Reselection, and Retraining.
Aaron Jiaxun Li, Robin Netzorg, Zhihan Cheng, Zhuoqin Zhang, Bin Yu
2024Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization.
Nayeong Kim, Juwon Kang, Sungsoo Ahn, Jungseul Ok, Suha Kwak
2024Improving SAM Requires Rethinking its Optimization Formulation.
Wanyun Xie, Fabian Latorre, Kimon Antonakopoulos, Thomas Pethick, Volkan Cevher
2024Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games.
Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang
2024Improving Sharpness-Aware Minimization by Lookahead.
Runsheng Yu, Youzhi Zhang, James T. Kwok
2024Improving Token-Based World Models with Parallel Observation Prediction.
Lior Cohen, Kaixin Wang, Bingyi Kang, Shie Mannor
2024Improving Transformers with Dynamically Composable Multi-Head Attention.
Da Xiao, Qingye Meng, Shengping Li, Xingyuan Yuan
2024Improving fine-grained understanding in image-text pre-training.
Ioana Bica, Anastasija Ilic, Matthias Bauer, Goker Erdogan, Matko Bosnjak, Christos Kaplanis, Alexey A. Gritsenko, Matthias Minderer, Charles Blundell, Razvan Pascanu, Jovana Mitrovic
2024In value-based deep reinforcement learning, a pruned network is a good network.
Johan S. Obando-Ceron, Aaron C. Courville, Pablo Samuel Castro
2024In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought.
Sili Huang, Jifeng Hu, Hechang Chen, Lichao Sun, Bo Yang
2024In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization.
Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Eddie Bergman, Frank Hutter
2024In-Context Language Learning: Architectures and Algorithms.
Ekin Akyürek, Bailin Wang, Yoon Kim, Jacob Andreas
2024In-Context Learning Agents Are Asymmetric Belief Updaters.
Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz, Eric Schulz
2024In-Context Principle Learning from Mistakes.
Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng, Swaroop Mishra, Yiming Yang, Niket Tandon, Uri Alon
2024In-Context Reinforcement Learning for Variable Action Spaces.
Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Sergey Kolesnikov
2024In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation.
Shiqi Chen, Miao Xiong, Junteng Liu, Zhengxuan Wu, Teng Xiao, Siyang Gao, Junxian He
2024In-Context Unlearning: Language Models as Few-Shot Unlearners.
Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju
2024In-context Convergence of Transformers.
Yu Huang, Yuan Cheng, Yingbin Liang
2024In-context Learning on Function Classes Unveiled for Transformers.
Zhijie Wang, Bo Jiang, Shuai Li
2024In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering.
Sheng Liu, Haotian Ye, Lei Xing, James Y. Zou
2024Incentivized Learning in Principal-Agent Bandit Games.
Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus
2024Incorporating Information into Shapley Values: Reweighting via a Maximum Entropy Approach.
Darya Biparva, Donatello Materassi
2024Incorporating probabilistic domain knowledge into deep multiple instance learning.
Ghadi S. Al Hajj, Aliaksandr Hubin, Chakravarthi Kanduri, Milena Pavlovic, Knut Dagestad Rand, Michael Widrich, Anne H. Schistad Solberg, Victor Greiff, Johan Pensar, Günter Klambauer, Geir Kjetil Sandve
2024Incremental Topological Ordering and Cycle Detection with Predictions.
Samuel McCauley, Benjamin Moseley, Aidin Niaparast, Shikha Singh
2024Indirectly Parameterized Concrete Autoencoders.
Alfred Nilsson, Klas Wijk, Sai Bharath Chandra Gutha, Erik Englesson, Alexandra Hotti, Carlo Saccardi, Oskar Kviman, Jens Lagergren, Ricardo Vinuesa, Hossein Azizpour
2024Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning.
Xinran Li, Zifan Liu, Shibo Chen, Jun Zhang
2024Individual Fairness in Graph Decomposition.
Kamesh Munagala, Govind S. Sankar
2024Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization.
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
2024Inexact Newton-type Methods for Optimisation with Nonnegativity Constraints.
Oscar Smee, Fred Roosta
2024InferCept: Efficient Intercept Support for Augmented Large Language Model Inference.
Reyna Abhyankar, Zijian He, Vikranth Srivatsa, Hao Zhang, Yiying Zhang
2024Inferring Change Points in High-Dimensional Linear Regression via Approximate Message Passing.
Gabriel Arpino, Xiaoqi Liu, Ramji Venkataramanan
2024Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting.
Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander
2024Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments.
Allen Tran, Aurélien Bibaut, Nathan Kallus
2024InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks.
Xueyu Hu, Ziyu Zhao, Shuang Wei, Ziwei Chai, Qianli Ma, Guoyin Wang, Xuwu Wang, Jing Su, Jingjing Xu, Ming Zhu, Yao Cheng, Jianbo Yuan, Jiwei Li, Kun Kuang, Yang Yang, Hongxia Yang, Fei Wu
2024Infinite-Horizon Distributionally Robust Regret-Optimal Control.
Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi
2024InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization.
Zhengyang Hu, Song Kang, Qunsong Zeng, Kaibin Huang, Yanchao Yang
2024Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing.
Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy
2024Information Flow in Self-Supervised Learning.
Zhiquan Tan, Jingqin Yang, Weiran Huang, Yang Yuan, Yifan Zhang
2024Information-Directed Pessimism for Offline Reinforcement Learning.
Alec Koppel, Sujay Bhatt, Jiacheng Guo, Joe Eappen, Mengdi Wang, Sumitra Ganesh
2024Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks.
Guanhua Zhang, Moritz Hardt
2024Initial Guessing Bias: How Untrained Networks Favor Some Classes.
Emanuele Francazi, Aurélien Lucchi, Marco Baity-Jesi
2024InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining.
Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro
2024InstructSpeech: Following Speech Editing Instructions via Large Language Models.
Rongjie Huang, Ruofan Hu, Yongqi Wang, Zehan Wang, Xize Cheng, Ziyue Jiang, Zhenhui Ye, Dongchao Yang, Luping Liu, Peng Gao, Zhou Zhao
2024InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models.
Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou
2024Instruction Tuning for Secure Code Generation.
Jingxuan He, Mark Vero, Gabriela Krasnopolska, Martin T. Vechev
2024Integrated Hardware Architecture and Device Placement Search.
Irene Wang, Jakub Tarnawski, Amar Phanishayee, Divya Mahajan
2024Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment.
Xudong Li, Runze Hu, Jingyuan Zheng, Yan Zhang, Shengchuan Zhang, Xiawu Zheng, Ke Li, Yunhang Shen, Yutao Liu, Pingyang Dai, Rongrong Ji
2024Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics.
Manuel Brenner, Florian Hess, Georgia Koppe, Daniel Durstewitz
2024InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning.
Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes
2024Interacting Diffusion Processes for Event Sequence Forecasting.
Mai Zeng, Florence Regol, Mark Coates
2024Interaction-based Retrieval-augmented Diffusion Models for Protein-specific 3D Molecule Generation.
Zhilin Huang, Ling Yang, Xiangxin Zhou, Chujun Qin, Yijie Yu, Xiawu Zheng, Zikun Zhou, Wentao Zhang, Yu Wang, Wenming Yang
2024Interplay of ROC and Precision-Recall AUCs: Theoretical Limits and Practical Implications in Binary Classification.
Martin Mihelich, François Castagnos, Charles Dognin
2024InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation.
Jacob Yoke Hong Si, Wendy Yusi Cheng, Michael Cooper, Rahul G. Krishnan
2024Interpretability Illusions in the Generalization of Simplified Models.
Dan Friedman, Andrew Kyle Lampinen, Lucas Dixon, Danqi Chen, Asma Ghandeharioun
2024Interpretable Deep Clustering for Tabular Data.
Jonathan Svirsky, Ofir Lindenbaum
2024Interpreting Equivariant Representations.
Andreas Abildtrup Hansen, Anna Calissano, Aasa Feragen
2024Interpreting and Improving Diffusion Models from an Optimization Perspective.
Frank Permenter, Chenyang Yuan
2024Interpreting and Improving Large Language Models in Arithmetic Calculation.
Wei Zhang, Chaoqun Wan, Yonggang Zhang, Yiu-ming Cheung, Xinmei Tian, Xu Shen, Jieping Ye
2024Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models.
Xiaofan Bai, Chaoxiang He, Xiaojing Ma, Bin Benjamin Zhu, Hai Jin
2024Intersectional Unfairness Discovery.
Gezheng Xu, Qi Chen, Charles Ling, Boyu Wang, Changjian Shui
2024Invariant Risk Minimization Is A Total Variation Model.
Zhao-Rong Lai, Weiwen Wang
2024Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects.
Aaron Fisher
2024Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning.
Donghu Kim, Hojoon Lee, Kyungmin Lee, Dongyoon Hwang, Jaegul Choo
2024Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach.
Weijia Zhang, Chenlong Yin, Hao Liu, Xiaofang Zhou, Hui Xiong
2024Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.
Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye, Weilin Liu, Zhiyu Mei, Guangju Wang, Chao Yu, Yi Wu
2024Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
2024Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective.
Fabian Falck, Ziyu Wang, Christopher C. Holmes
2024Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective.
Lei Zhao, Mengdi Wang, Yu Bai
2024Is Kernel Prediction More Powerful than Gating in Convolutional Neural Networks?
Lorenz K. Müller
2024Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
Huy Nguyen, Pedram Akbarian, Nhat Ho
2024Isometric Representation Learning for Disentangled Latent Space of Diffusion Models.
Jaehoon Hahm, Junho Lee, Sunghyun Kim, Joonseok Lee
2024Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong
2024Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF.
Banghua Zhu, Michael I. Jordan, Jiantao Jiao
2024Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint.
Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang
2024Iterative Regularized Policy Optimization with Imperfect Demonstrations.
Xudong Gong, Dawei Feng, Kele Xu, Yuanzhao Zhai, Chengkang Yao, Weijia Wang, Bo Ding, Huaimin Wang
2024Iterative Search Attribution for Deep Neural Networks.
Zhiyu Zhu, Huaming Chen, Xinyi Wang, Jiayu Zhang, Zhibo Jin, Jason Xue, Jun Shen
2024Jacobian Regularizer-based Neural Granger Causality.
Wanqi Zhou, Shuanghao Bai, Shujian Yu, Qibin Zhao, Badong Chen
2024Jetfire: Efficient and Accurate Transformer Pretraining with INT8 Data Flow and Per-Block Quantization.
Haocheng Xi, Yuxiang Chen, Kang Zhao, Kai Jun Teh, Jianfei Chen, Jun Zhu
2024Joint Composite Latent Space Bayesian Optimization.
Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy
2024Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs.
Lu Yin, Ajay Kumar Jaiswal, Shiwei Liu, Souvik Kundu, Zhangyang Wang
2024Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations.
Stefan Sylvius Wagner, Stefan Harmeling
2024KISA: A Unified Keyframe Identifier and Skill Annotator for Long-Horizon Robotics Demonstrations.
Longxin Kou, Fei Ni, Yan Zheng, Jinyi Liu, Yifu Yuan, Zibin Dong, Jianye Hao
2024KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache.
Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen (Henry) Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, Xia Hu
2024KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation.
Minsik Cho, Mohammad Rastegari, Devang Naik
2024Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows.
Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré
2024Kepler codebook.
Junrong Lian, Ziyue Dong, Pengxu Wei, Wei Ke, Chang Liu, Qixiang Ye, Xiangyang Ji, Liang Lin
2024Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters.
Brian M. Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica
2024Kernel Semi-Implicit Variational Inference.
Ziheng Cheng, Longlin Yu, Tianyu Xie, Shiyue Zhang, Cheng Zhang
2024Kernel-Based Evaluation of Conditional Biological Sequence Models.
Pierre Glaser, Steffanie Paul, Alissa M. Hummer, Charlotte M. Deane, Debora Susan Marks, Alan Nawzad Amin
2024KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer
2024KernelWarehouse: Rethinking the Design of Dynamic Convolution.
Chao Li, Anbang Yao
2024Keypoint-based Progressive Chain-of-Thought Distillation for LLMs.
Kaituo Feng, Changsheng Li, Xiaolu Zhang, Jun Zhou, Ye Yuan, Guoren Wang
2024KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning.
Junnan Liu, Qianren Mao, Weifeng Jiang, Jianxin Li
2024Knowledge Distillation with Auxiliary Variable.
Bo Peng, Zhen Fang, Guangquan Zhang, Jie Lu
2024Knowledge Graphs Can be Learned with Just Intersection Features.
Duy Le, Shaochen (Henry) Zhong, Zirui Liu, Shuai Xu, Vipin Chaudhary, Kaixiong Zhou, Zhaozhuo Xu
2024Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models.
Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari, Oncel Tuzel
2024Knowledge-aware Reinforced Language Models for Protein Directed Evolution.
Yuhao Wang, Qiang Zhang, Ming Qin, Xiang Zhuang, Xiaotong Li, Zhichen Gong, Zeyuan Wang, Yu Zhao, Jianhua Yao, Keyan Ding, Huajun Chen
2024LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning.
Hyungho Na, Il-Chul Moon
2024LASER: Linear Compression in Wireless Distributed Optimization.
Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael Gastpar
2024LCA-on-the-Line: Benchmarking Out of Distribution Generalization with Class Taxonomies.
Jia Shi, Gautam Rajendrakumar Gare, Jinjin Tian, Siqi Chai, Zhiqiu Lin, Arun Balajee Vasudevan, Di Feng, Francesco Ferroni, Shu Kong
2024LESS: Selecting Influential Data for Targeted Instruction Tuning.
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, Danqi Chen
2024LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views.
Yuji Roh, Qingyun Liu, Huan Gui, Zhe Yuan, Yujin Tang, Steven Euijong Whang, Liang Liu, Shuchao Bi, Lichan Hong, Ed H. Chi, Zhe Zhao
2024LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models.
Tianci Liu, Haoyu Wang, Shiyang Wang, Yu Cheng, Jing Gao
2024LLM Maybe LongLM: SelfExtend LLM Context Window Without Tuning.
Hongye Jin, Xiaotian Han, Jingfeng Yang, Zhimeng Jiang, Zirui Liu, Chia-Yuan Chang, Huiyuan Chen, Xia Hu
2024LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery.
Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo, Zhiqing Sun, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan, Wojciech Matusik
2024LLM-Empowered State Representation for Reinforcement Learning.
Boyuan Wang, Yun Qu, Yuhang Jiang, Jianzhun Shao, Chang Liu, Wenming Yang, Xiangyang Ji
2024LLaGA: Large Language and Graph Assistant.
Runjin Chen, Tong Zhao, Ajay Kumar Jaiswal, Neil Shah, Zhangyang Wang
2024LLark: A Multimodal Instruction-Following Language Model for Music.
Joshua Patrick Gardner, Simon Durand, Daniel Stoller, Rachel M. Bittner
2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layers.
Anselm Paulus, Georg Martius, Vít Musil
2024LQER: Low-Rank Quantization Error Reconstruction for LLMs.
Cheng Zhang, Jianyi Cheng, George Anthony Constantinides, Yiren Zhao
2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering.
Li Sun, Zhenhao Huang, Hao Peng, Yujie Wang, Chunyang Liu, Philip S. Yu
2024LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits.
Chen-Chia Chang, Yikang Shen, Shaoze Fan, Jing Li, Shun Zhang, Ningyuan Cao, Yiran Chen, Xin Zhang
2024LangCell: Language-Cell Pre-training for Cell Identity Understanding.
Suyuan Zhao, Jiahuan Zhang, Yushuai Wu, Yizhen Luo, Zaiqing Nie
2024Langevin Policy for Safe Reinforcement Learning.
Fenghao Lei, Long Yang, Shiting Wen, Zhixiong Huang, Zhiwang Zhang, Chaoyi Pang
2024Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models.
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, Yu-Xiong Wang
2024Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game.
Zelai Xu, Chao Yu, Fei Fang, Yu Wang, Yi Wu
2024Language Generation with Strictly Proper Scoring Rules.
Chenze Shao, Fandong Meng, Yijin Liu, Jie Zhou
2024Language Models Represent Beliefs of Self and Others.
Wentao Zhu, Zhining Zhang, Yizhou Wang
2024Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch.
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li
2024Language Models as Science Tutors.
Alexis Chevalier, Jiayi Geng, Alexander Wettig, Howard Chen, Sebastian Mizera, Toni Annala, Max Jameson Aragon, Arturo Rodríguez Fanlo, Simon Frieder, Simon Machado, Akshara Prabhakar, Ellie Thieu, Jiachen T. Wang, Zirui Wang, Xindi Wu, Mengzhou Xia, Wenhan Xia, Jiatong Yu, Junjie Zhu, Zhiyong Jason Ren, Sanjeev Arora, Danqi Chen
2024Language Models as Semantic Indexers.
Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, Xianfeng Tang
2024Language Models with Conformal Factuality Guarantees.
Christopher Mohri, Tatsunori Hashimoto
2024Language-Driven Cross-Modal Classifier for Zero-Shot Multi-Label Image Recognition.
Yicheng Liu, Jie Wen, Chengliang Liu, Xiaozhao Fang, Zuoyong Li, Yong Xu, Zheng Zhang
2024Language-guided Skill Learning with Temporal Variational Inference.
Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin, George Konidaris, Nicolas Le Roux, Marc-Alexandre Côté, Xingdi Yuan
2024Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning.
Sungwon Han, Jinsung Yoon, Sercan Ö. Arik, Tomas Pfister
2024Large Language Models are Geographically Biased.
Rohin Manvi, Samar Khanna, Marshall Burke, David B. Lobell, Stefano Ermon
2024Large Scale Dataset Distillation with Domain Shift.
Noel Loo, Alaa Maalouf, Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus
2024Larimar: Large Language Models with Episodic Memory Control.
Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarathkrishna Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen
2024Latent Logic Tree Extraction for Event Sequence Explanation from LLMs.
Zitao Song, Chao Yang, Chaojie Wang, Bo An, Shuang Li
2024Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping.
Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog
2024Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming.
Xinlei Niu, Christian Walder, Jing Zhang, Charles Patrick Martin
2024Latent Space Symmetry Discovery.
Jianke Yang, Nima Dehmamy, Robin Walters, Rose Yu
2024Latent variable model for high-dimensional point process with structured missingness.
Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
2024Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency.
Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu
2024LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging.
Jinuk Kim, Marwa El Halabi, Mingi Ji, Hyun Oh Song
2024Layerwise Change of Knowledge in Neural Networks.
Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han, Quanshi Zhang
2024Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning.
Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss
2024LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions.
Victor Agostinelli, Sanghyun Hong, Lizhong Chen
2024Learning 1-Bit Tiny Object Detector with Discriminative Feature Refinement.
Sheng Xu, Mingze Wang, Yanjing Li, Mingbao Lin, Baochang Zhang, David S. Doermann, Xiao Sun
2024Learning Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking.
Yongxin Li, Mengyuan Liu, You Wu, Xucheng Wang, Xiangyang Yang, Shuiwang Li
2024Learning Associative Memories with Gradient Descent.
Vivien Cabannes, Berfin Simsek, Alberto Bietti
2024Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition.
Yuke Li, Guangyi Chen, Ben Abramowitz, Stefano Anzellotti, Donglai Wei
2024Learning Causal Dynamics Models in Object-Oriented Environments.
Zhongwei Yu, Jingqing Ruan, Dengpeng Xing
2024Learning Causal Relations from Subsampled Time Series with Two Time-Slices.
Anpeng Wu, Haoxuan Li, Kun Kuang, Keli Zhang, Fei Wu
2024Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments.
Antoine Dedieu, Wolfgang Lehrach, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla
2024Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation.
Guorui Quan, Zhiqiang Xu, Guiliang Liu
2024Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning.
Arvi Jonnarth, Jie Zhao, Michael Felsberg
2024Learning Decision Policies with Instrumental Variables through Double Machine Learning.
Daqian Shao, Ashkan Soleymani, Francesco Quinzan, Marta Kwiatkowska
2024Learning Decision Trees and Forests with Algorithmic Recourse.
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, Yuichi Ike
2024Learning Divergence Fields for Shift-Robust Graph Representations.
Qitian Wu, Fan Nie, Chenxiao Yang, Junchi Yan
2024Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence.
Sascha Xu, Nils Philipp Walter, Janis Kalofolias, Jilles Vreeken
2024Learning Graph Representation via Graph Entropy Maximization.
Ziheng Sun, Xudong Wang, Chris Ding, Jicong Fan
2024Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding.
Chuanhao Sun, Zhihang Yuan, Kai Xu, Luo Mai, N. Siddharth, Shuo Chen, Mahesh K. Marina
2024Learning High-Order Relationships of Brain Regions.
Weikang Qiu, Huangrui Chu, Selena Wang, Haolan Zuo, Xiaoxiao Li, Yize Zhao, Rex Ying
2024Learning Iterative Reasoning through Energy Diffusion.
Yilun Du, Jiayuan Mao, Joshua B. Tenenbaum
2024Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition.
Tong Wei, Zhen Mao, Zi-Hao Zhou, Yuanyu Wan, Min-Ling Zhang
2024Learning Latent Dynamic Robust Representations for World Models.
Ruixiang Sun, Hongyu Zang, Xin Li, Riashat Islam
2024Learning Latent Space Hierarchical EBM Diffusion Models.
Jiali Cui, Tian Han
2024Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders.
Xue Yu, Muchen Li, Yan Leng, Renjie Liao
2024Learning Linear Block Error Correction Codes.
Yoni Choukroun, Lior Wolf
2024Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds.
Yuyang Zhang, Shahriar Talebi, Na Li
2024Learning Mixtures of Gaussian Processes through Random Projection.
Emmanuel Akeweje, Mimi Zhang
2024Learning Modality Knowledge Alignment for Cross-Modality Transfer.
Wenxuan Ma, Shuang Li, Lincan Cai, Jingxuan Kang
2024Learning Multiple Secrets in Mastermind.
Milind Prabhu, David P. Woodruff
2024Learning Optimal Deterministic Policies with Stochastic Policy Gradients.
Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli, Matteo Papini
2024Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series.
Asterios Tsiourvas, Wei Sun, Georgia Perakis, Pin-Yu Chen, Yada Zhu
2024Learning Pseudo-Contractive Denoisers for Inverse Problems.
Deliang Wei, Peng Chen, Fang Li
2024Learning Reward for Robot Skills Using Large Language Models via Self-Alignment.
Yuwei Zeng, Yao Mu, Lin Shao
2024Learning Scale-Aware Spatio-temporal Implicit Representation for Event-based Motion Deblurring.
Wei Yu, Jianing Li, Shengping Zhang, Xiangyang Ji
2024Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias.
Baohong Li, Haoxuan Li, Ruoxuan Xiong, Anpeng Wu, Fei Wu, Kun Kuang
2024Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem.
Zhentao Tan, Yadong Mu
2024Learning Surrogates for Offline Black-Box Optimization via Gradient Matching.
Minh Hoang, Azza Fadhel, Aryan Deshwal, Jana Doppa, Trong Nghia Hoang
2024Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making.
Vivek Myers, Chongyi Zheng, Anca D. Dragan, Sergey Levine, Benjamin Eysenbach
2024Learning Universal Predictors.
Jordi Grau-Moya, Tim Genewein, Marcus Hutter, Laurent Orseau, Grégoire Delétang, Elliot Catt, Anian Ruoss, Li Kevin Wenliang, Christopher Mattern, Matthew Aitchison, Joel Veness
2024Learning Useful Representations of Recurrent Neural Network Weight Matrices.
Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber
2024Learning a Diffusion Model Policy from Rewards via Q-Score Matching.
Michael Psenka, Alejandro Escontrela, Pieter Abbeel, Yi Ma
2024Learning and Forgetting Unsafe Examples in Large Language Models.
Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren
2024Learning from Integral Losses in Physics Informed Neural Networks.
Ehsan Saleh, Saba Ghaffari, Timothy Bretl, Luke N. Olson, Matthew West
2024Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features.
Thalles Silva, Hélio Pedrini, Adín Ramírez Rivera
2024Learning from Streaming Data when Users Choose.
Jinyan Su, Sarah Dean
2024Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs.
Jordan Dotzel, Yuzong Chen, Bahaa Kotb, Sushma Prasad, Gang Wu, Sheng Li, Mohamed S. Abdelfattah, Zhiru Zhang
2024Learning in Deep Factor Graphs with Gaussian Belief Propagation.
Seth Nabarro, Mark van der Wilk, Andrew J. Davison
2024Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method.
Qinghua Tao, Francesco Tonin, Alex Lambert, Yingyi Chen, Panagiotis Patrinos, Johan A. K. Suykens
2024Learning the Target Network in Function Space.
Kavosh Asadi, Yao Liu, Shoham Sabach, Ming Yin, Rasool Fakoor
2024Learning the Uncertainty Sets of Linear Control Systems via Set Membership: A Non-asymptotic Analysis.
Yingying Li, Jing Yu, Lauren E. Conger, Taylan Kargin, Adam Wierman
2024Learning to Compile Programs to Neural Networks.
Logan Weber, Jesse Michel, Alex Renda, Michael Carbin
2024Learning to Continually Learn with the Bayesian Principle.
Soochan Lee, Hyeonseong Jeon, Jaehyeon Son, Gunhee Kim
2024Learning to Explore for Stochastic Gradient MCMC.
Seunghyun Kim, Seohyeon Jung, Seonghyeon Kim, Juho Lee
2024Learning to Explore in POMDPs with Informational Rewards.
Annie Xie, Logan M. Bhamidipaty, Evan Zheran Liu, Joey Hong, Sergey Levine, Chelsea Finn
2024Learning to Infer Generative Template Programs for Visual Concepts.
R. Kenny Jones, Siddhartha Chaudhuri, Daniel Ritchie
2024Learning to Intervene on Concept Bottlenecks.
David Steinmann, Wolfgang Stammer, Felix Friedrich, Kristian Kersting
2024Learning to Model the World With Language.
Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, Anca D. Dragan
2024Learning to Play Atari in a World of Tokens.
Pranav Agarwal, Sheldon Andrews, Samira Ebrahimi Kahou
2024Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning.
Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li
2024Learning to Reach Goals via Diffusion.
Vineet Jain, Siamak Ravanbakhsh
2024Learning to Remove Cuts in Integer Linear Programming.
Pol Puigdemont, Stratis Skoulakis, Grigorios Chrysos, Volkan Cevher
2024Learning to Route Among Specialized Experts for Zero-Shot Generalization.
Mohammed Muqeeth, Haokun Liu, Yufan Liu, Colin Raffel
2024Learning to Scale Logits for Temperature-Conditional GFlowNets.
Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, Emmanuel Bengio, Yoshua Bengio
2024Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces.
Brahma S. Pavse, Matthew Zurek, Yudong Chen, Qiaomin Xie, Josiah P. Hanna
2024Learning with 3D rotations, a hitchhiker's guide to SO(3).
Andreas René Geist, Jonas Frey, Mikel Zhobro, Anna Levina, Georg Martius
2024Learning with Adaptive Resource Allocation.
Jing Wang, Miao Yu, Peng Zhao, Zhi-Hua Zhou
2024Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical.
Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama
2024Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency.
Yangfan Liu, Jiaqi Lv, Xin Geng, Ning Xu
2024Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation.
Yuanhao Pu, Xiaolong Chen, Xu Huang, Jin Chen, Defu Lian, Enhong Chen
2024Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds.
Daniel Dodd, Louis Sharrock, Christopher Nemeth
2024Less is More: on the Over-Globalizing Problem in Graph Transformers.
Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, Chuan Shi
2024Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!
Milad Sefidgaran, Romain Chor, Abdellatif Zaidi, Yijun Wan
2024Let Go of Your Labels with Unsupervised Transfer.
Artyom Gadetsky, Yulun Jiang, Maria Brbic
2024Leverage Class-Specific Accuracy to Guide Data Generation for Improving Image Classification.
Jay Gala, Pengtao Xie
2024Leveraging (Biased) Information: Multi-armed Bandits with Offline Data.
Wang Chi Cheung, Lixing Lyu
2024Leveraging Attractor Dynamics in Spatial Navigation for Better Language Parsing.
Xiaolong Zou, Xingxing Cao, Xiaojiao Yang, Bo Hong
2024Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference.
Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev
2024Leveraging VLM-Based Pipelines to Annotate 3D Objects.
Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra
2024Libra: Building Decoupled Vision System on Large Language Models.
Yifan Xu, Xiaoshan Yang, Yaguang Song, Changsheng Xu
2024Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras.
Tzu-Yuan Lin, Minghan Zhu, Maani Ghaffari
2024Light and Optimal Schrödinger Bridge Matching.
Nikita Gushchin, Sergei Kholkin, Evgeny Burnaev, Alexander Korotin
2024Lightweight Image Super-Resolution via Flexible Meta Pruning.
Yulun Zhang, Kai Zhang, Luc Van Gool, Martin Danelljan, Fisher Yu
2024Limited Preference Aided Imitation Learning from Imperfect Demonstrations.
Xingchen Cao, Fan-Ming Luo, Junyin Ye, Tian Xu, Zhilong Zhang, Yang Yu
2024Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback.
Songyang Gao, Qiming Ge, Wei Shen, Shihan Dou, Junjie Ye, Xiao Wang, Rui Zheng, Yicheng Zou, Zhi Chen, Hang Yan, Qi Zhang, Dahua Lin
2024Linear Explanations for Individual Neurons.
Tuomas P. Oikarinen, Tsui-Wei Weng
2024Linguistic Calibration of Long-Form Generations.
Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto
2024Liouville Flow Importance Sampler.
Yifeng Tian, Nishant Panda, Yen Ting Lin
2024Listenable Maps for Audio Classifiers.
Francesco Paissan, Mirco Ravanelli, Cem Subakan
2024Listening to the noise: Blind Denoising with Gibbs Diffusion.
David Heurtel-Depeiges, Charles Margossian, Ruben Ohana, Bruno Régaldo-Saint Blancard
2024Listwise Reward Estimation for Offline Preference-based Reinforcement Learning.
Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon
2024LoCoCo: Dropping In Convolutions for Long Context Compression.
Ruisi Cai, Yuandong Tian, Zhangyang Wang, Beidi Chen
2024LoRA Training in the NTK Regime has No Spurious Local Minima.
Uijeong Jang, Jason D. Lee, Ernest K. Ryu
2024LoRA+: Efficient Low Rank Adaptation of Large Models.
Soufiane Hayou, Nikhil Ghosh, Bin Yu
2024LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models.
Guangyan Li, Yongqiang Tang, Wensheng Zhang
2024Local Causal Structure Learning in the Presence of Latent Variables.
Feng Xie, Zheng Li, Peng Wu, Yan Zeng, Chunchen Liu, Zhi Geng
2024Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions.
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
2024Local vs. Global Interpretability: A Computational Complexity Perspective.
Shahaf Bassan, Guy Amir, Guy Katz
2024Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics.
Siqi Miao, Zhiyuan Lu, Mia Liu, Javier M. Duarte, Pan Li
2024Localizing Task Information for Improved Model Merging and Compression.
Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jiménez, François Fleuret, Pascal Frossard
2024Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy.
Ziqin Chen, Yongqiang Wang
2024Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization.
Ziqing Fan, Shengchao Hu, Jiangchao Yao, Gang Niu, Ya Zhang, Masashi Sugiyama, Yanfeng Wang
2024Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies.
Alex DeWeese, Guannan Qu
2024Log Neural Controlled Differential Equations: The Lie Brackets Make A Difference.
Benjamin Walker, Andrew D. McLeod, Tiexin Qin, Yichuan Cheng, Haoliang Li, Terry J. Lyons
2024Logistic Variational Bayes Revisited.
Michael Komodromos, Marina Evangelou, Sarah Filippi
2024Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning.
Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion
2024Long Range Propagation on Continuous-Time Dynamic Graphs.
Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio, Davide Bacciu, Claas Grohnfeldt
2024Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts.
Jiang-Xin Shi, Tong Wei, Zhi Zhou, Jie-Jing Shao, Xin-Yan Han, Yufeng Li
2024LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens.
Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, Mao Yang
2024Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer.
Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li, Mingduo Zhao, Hiroyasu Iso, Mark J. van der Laan
2024Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked Pretraining.
Qi Zhang, Tianqi Du, Haotian Huang, Yifei Wang, Yisen Wang
2024Lookbehind-SAM: k steps back, 1 step forward.
Gonçalo Mordido, Pranshu Malviya, Aristide Baratin, Sarath Chandar
2024Loss Shaping Constraints for Long-Term Time Series Forecasting.
Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro
2024Low-Cost High-Power Membership Inference Attacks.
Sajjad Zarifzadeh, Philippe Liu, Reza Shokri
2024Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery.
Yassir Jedra, William Réveillard, Stefan Stojanovic, Alexandre Proutière
2024Low-Rank Similarity Mining for Multimodal Dataset Distillation.
Yue Xu, Zhilin Lin, Yusong Qiu, Cewu Lu, Yong-Lu Li
2024Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation.
Lujie Yang, Hongkai Dai, Zhouxing Shi, Cho-Jui Hsieh, Russ Tedrake, Huan Zhang
2024MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent.
Kaan Ozkara, Can Karakus, Parameswaran Raman, Mingyi Hong, Shoham Sabach, Branislav Kveton, Volkan Cevher
2024MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models.
Justin Chih-Yao Chen, Swarnadeep Saha, Elias Stengel-Eskin, Mohit Bansal
2024MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation.
Alexandre Hayderi, Amin Saberi, Ellen Vitercik, Anders Wikum
2024MALIBO: Meta-learning for Likelihood-free Bayesian Optimization.
Jiarong Pan, Stefan Falkner, Felix Berkenkamp, Joaquin Vanschoren
2024MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations.
Zhangyu Wang, Gengchen Mai, Krzysztof Janowicz, Ni Lao
2024MD tree: a model-diagnostic tree grown on loss landscape.
Yefan Zhou, Jianlong Chen, Qinxue Cao, Konstantin Schürholt, Yaoqing Yang
2024MEMORYLLM: Towards Self-Updatable Large Language Models.
Yu Wang, Yifan Gao, Xiusi Chen, Haoming Jiang, Shiyang Li, Jingfeng Yang, Qingyu Yin, Zheng Li, Xian Li, Bing Yin, Jingbo Shang, Julian J. McAuley
2024MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series.
Jufang Duan, Wei Zheng, Yangzhou Du, Wenfa Wu, Haipeng Jiang, Hongsheng Qi
2024MFTN: A Multi-scale Feature Transfer Network Based on IMatchFormer for Hyperspectral Image Super-Resolution.
Shuying Huang, Mingyang Ren, Yong Yang, Xiaozheng Wang, Yingzhi Wei
2024MGit: A Model Versioning and Management System.
Wei Hao, Daniel Mendoza, Rafael Mendes, Deepak Narayanan, Amar Phanishayee, Asaf Cidon, Junfeng Yang
2024MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis.
Luyuan Xie, Manqing Lin, Tianyu Luan, Cong Li, Yuejian Fang, Qingni Shen, Zhonghai Wu
2024MILP-FBGen: LP/MILP Instance Generation with Feasibility/Boundedness.
Yahong Zhang, Chenchen Fan, Donghui Chen, Congrui Li, Wenli Ouyang, Mingda Zhu, Junchi Yan
2024MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation.
Qian Huang, Jian Vora, Percy Liang, Jure Leskovec
2024MLI Formula: A Nearly Scale-Invariant Solution with Noise Perturbation.
Bowen Tao, Xin-Chun Li, De-Chuan Zhan
2024MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization.
Yu Zhang, Qi Zhang, Zixuan Gong, Yiwei Shi, Yepeng Liu, Duoqian Miao, Yang Liu, Ke Liu, Kun Yi, Wei Fan, Liang Hu, Changwei Wang
2024MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark.
Dongping Chen, Ruoxi Chen, Shilin Zhang, Yaochen Wang, Yinuo Liu, Huichi Zhou, Qihui Zhang, Yao Wan, Pan Zhou, Lichao Sun
2024MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, Lijuan Wang
2024MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance.
Yake Wei, Di Hu
2024MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI.
Kaining Ying, Fanqing Meng, Jin Wang, Zhiqian Li, Han Lin, Yue Yang, Hao Zhang, Wenbo Zhang, Yuqi Lin, Shuo Liu, Jiayi Lei, Quanfeng Lu, Runjian Chen, Peng Xu, Renrui Zhang, Haozhe Zhang, Peng Gao, Yali Wang, Yu Qiao, Ping Luo, Kaipeng Zhang, Wenqi Shao
2024MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence.
Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han
2024MOMENT: A Family of Open Time-series Foundation Models.
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski
2024MS-TIP: Imputation Aware Pedestrian Trajectory Prediction.
Pranav Singh Chib, Achintya Nath, Paritosh Kabra, Ishu Gupta, Pravendra Singh
2024MS3D: A RG Flow-Based Regularization for GAN Training with Limited Data.
Jian Wang, Xin Lan, Yuxin Tian, Jiancheng Lv
2024MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts.
Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, Chi Xu
2024MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective.
Yizhuo Chen, Chun-Fu Chen, Hsiang Hsu, Shaohan Hu, Marco Pistoia, Tarek F. Abdelzaher
2024Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning.
Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu
2024Maestro: Uncovering Low-Rank Structures via Trainable Decomposition.
Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang
2024MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions.
Kai Zhang, Yi Luan, Hexiang Hu, Kenton Lee, Siyuan Qiao, Wenhu Chen, Yu Su, Ming-Wei Chang
2024MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion.
Di Chang, Yichun Shi, Quankai Gao, Hongyi Xu, Jessica Fu, Guoxian Song, Qing Yan, Yizhe Zhu, Xiao Yang, Mohammad Soleymani
2024Magicoder: Empowering Code Generation with OSS-Instruct.
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, Lingming Zhang
2024Major-Minor Mean Field Multi-Agent Reinforcement Learning.
Kai Cui, Christian Fabian, Anam Tahir, Heinz Koeppl
2024Make-A-Shape: a Ten-Million-scale 3D Shape Model.
Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, Chi-Wing Fu
2024Making Old Things New: A Unified Algorithm for Differentially Private Clustering.
Max Dupré la Tour, Monika Henzinger, David Saulpic
2024Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution.
Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta
2024Mapping the Multiverse of Latent Representations.
Jeremy Wayland, Corinna Coupette, Bastian Rieck
2024Masked Face Recognition with Generative-to-Discriminative Representations.
Shiming Ge, Weijia Guo, Chenyu Li, Junzheng Zhang, Yong Li, Dan Zeng
2024Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning.
Jiachen Li, Qiaozi Gao, Michael Johnston, Xiaofeng Gao, Xuehai He, Hangjie Shi, Suhaila Shakiah, Reza Ghanadan, William Yang Wang
2024Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs.
Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui
2024Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games.
Yannik Mahlau, Frederik Schubert, Bodo Rosenhahn
2024MathScale: Scaling Instruction Tuning for Mathematical Reasoning.
Zhengyang Tang, Xingxing Zhang, Benyou Wang, Furu Wei
2024Matrix Information Theory for Self-Supervised Learning.
Yifan Zhang, Zhiquan Tan, Jingqin Yang, Weiran Huang, Yang Yuan
2024Matroid Semi-Bandits in Sublinear Time.
Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu
2024MaxMin-RLHF: Alignment with Diverse Human Preferences.
Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Dinesh Manocha, Furong Huang, Amrit S. Bedi, Mengdi Wang
2024Mean Estimation in the Add-Remove Model of Differential Privacy.
Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
2024Mean Field Langevin Actor-Critic: Faster Convergence and Global Optimality beyond Lazy Learning.
Kakei Yamamoto, Kazusato Oko, Zhuoran Yang, Taiji Suzuki
2024Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective.
Shokichi Takakura, Taiji Suzuki
2024Mean-field Chaos Diffusion Models.
Sungwoo Park, Dongjun Kim, Ahmed Alaa
2024Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization.
Qiang Fu, Ashia Camage Wilson
2024Measures of diversity and space-filling designs for categorical data.
Cédric Malherbe, Emilio Domínguez-Sánchez, Merwan Barlier, Igor Colin, Haitham Bou-Ammar, Tom Diethe
2024Measuring Stochastic Data Complexity with Boltzmann Influence Functions.
Nathan H. Ng, Roger Baker Grosse, Marzyeh Ghassemi
2024Mechanistic Design and Scaling of Hybrid Architectures.
Michael Poli, Armin W. Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian L. Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli
2024Mechanistic Neural Networks for Scientific Machine Learning.
Adeel Pervez, Francesco Locatello, Stratis Gavves
2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads.
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, Tri Dao
2024Membership Inference Attacks on Diffusion Models via Quantile Regression.
Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth
2024Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture.
Sangjun Park, JinYeong Bak
2024Memorization Through the Lens of Curvature of Loss Function Around Samples.
Isha Garg, Deepak Ravikumar, Kaushik Roy
2024Memory Consolidation Enables Long-Context Video Understanding.
Ivana Balazevic, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, Olivier J. Hénaff
2024Memory Efficient Neural Processes via Constant Memory Attention Block.
Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Yoshua Bengio, Mohamed Osama Ahmed
2024Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning.
Shibo Jie, Yehui Tang, Ning Ding, Zhi-Hong Deng, Kai Han, Yunhe Wang
2024Merging Multi-Task Models via Weight-Ensembling Mixture of Experts.
Anke Tang, Li Shen, Yong Luo, Nan Yin, Lefei Zhang, Dacheng Tao
2024Meta Evidential Transformer for Few-Shot Open-Set Recognition.
Hitesh Sapkota, Krishna Prasad Neupane, Qi Yu
2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments.
Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel
2024Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning.
Tengye Xu, Zihao Li, Qinyuan Ren
2024Mimicking Better by Matching the Approximate Action Distribution.
João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi, Alexandros Kalousis
2024Mind the Boundary: Coreset Selection via Reconstructing the Decision Boundary.
Shuo Yang, Zhe Cao, Sheng Guo, Ruiheng Zhang, Ping Luo, Shengping Zhang, Liqiang Nie
2024MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data.
Paul S. Scotti, Mihir Tripathy, Cesar Torrico, Reese Kneeland, Tong Chen, Ashutosh Narang, Charan Santhirasegaran, Jonathan Xu, Thomas Naselaris, Kenneth A. Norman, Tanishq Mathew Abraham
2024Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value.
Young Wu, Jeremy McMahan, Yiding Chen, Yudong Chen, Jerry Zhu, Qiaomin Xie
2024Minimax Optimality of Score-based Diffusion Models: Beyond the Density Lower Bound Assumptions.
Kaihong Zhang, Heqi Yin, Feng Liang, Jingbo Liu
2024Minimizing f-Divergences by Interpolating Velocity Fields.
Song Liu, Jiahao Yu, Jack Simons, Mingxuan Yi, Mark Beaumont
2024Minimum Norm Interpolation Meets The Local Theory of Banach Spaces.
Gil Kur, Pedro Abdalla, Pierre Bizeul, Fanny Yang
2024Minimum-Norm Interpolation Under Covariate Shift.
Neil Mallinar, Austin Zane, Spencer Frei, Bin Yu
2024Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations via Pareto Optimization.
Yichen Wu, Hong Wang, Peilin Zhao, Yefeng Zheng, Ying Wei, Long-Kai Huang
2024Mitigating Label Noise on Graphs via Topological Sample Selection.
Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu
2024Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs.
MoonJeong Park, Jaeseung Heo, Dongwoo Kim
2024Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss.
Zhenlong Liu, Lei Feng, Huiping Zhuang, Xiaofeng Cao, Hongxin Wei
2024Mixtures of Experts Unlock Parameter Scaling for Deep RL.
Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro
2024MoE-RBench: Towards Building Reliable Language Models with Sparse Mixture-of-Experts.
Guanjie Chen, Xinyu Zhao, Tianlong Chen, Yu Cheng
2024MoMo: Momentum Models for Adaptive Learning Rates.
Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
2024Mobile Attention: Mobile-Friendly Linear-Attention for Vision Transformers.
Zhiyu Yao, Jian Wang, Haixu Wu, Jingdong Wang, Mingsheng Long
2024MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases.
Zechun Liu, Changsheng Zhao, Forrest N. Iandola, Chen Lai, Yuandong Tian, Igor Fedorov, Yunyang Xiong, Ernie Chang, Yangyang Shi, Raghuraman Krishnamoorthi, Liangzhen Lai, Vikas Chandra
2024Model Alignment as Prospect Theoretic Optimization.
Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, Douwe Kiela
2024Model Assessment and Selection under Temporal Distribution Shift.
Elise Han, Chengpiao Huang, Kaizheng Wang
2024Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models.
Didi Zhu, Zhongyi Sun, Zexi Li, Tao Shen, Ke Yan, Shouhong Ding, Chao Wu, Kun Kuang
2024Model-Based Minimum Bayes Risk Decoding for Text Generation.
Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, Kenshi Abe
2024Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL.
Jiawei Huang, Niao He, Andreas Krause
2024Model-Free Robust ϕ-Divergence Reinforcement Learning Using Both Offline and Online Data.
Kishan Panaganti, Adam Wierman, Eric Mazumdar
2024Model-based Reinforcement Learning for Confounded POMDPs.
Mao Hong, Zhengling Qi, Yanxun Xu
2024Model-based Reinforcement Learning for Parameterized Action Spaces.
Renhao Zhang, Haotian Fu, Yilin Miao, George Konidaris
2024Modeling Caption Diversity in Contrastive Vision-Language Pretraining.
Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mido Assran, Andrew Gordon Wilson, Aaron C. Courville, Nicolas Ballas
2024Modeling Language Tokens as Functionals of Semantic Fields.
Zhengqi Pei, Anran Zhang, Shuhui Wang, Qingming Huang
2024Modelling Microbial Communities with Graph Neural Networks.
Albane Ruaud, Cansu Sancaktar, Marco Bagatella, Christoph Ratzke, Georg Martius
2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference.
Md. Musfiqur Rahman, Murat Kocaoglu
2024Mol-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective.
Junwei Yang, Kangjie Zheng, Siyu Long, Zaiqing Nie, Ming Zhang, Xinyu Dai, Wei-Ying Ma, Hao Zhou
2024MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space.
Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, Wei-Ying Ma
2024Mollification Effects of Policy Gradient Methods.
Tao Wang, Sylvia L. Herbert, Sicun Gao
2024Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning.
Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei, Tat-Seng Chua, Yueting Zhuang, Siliang Tang
2024Momentum Particle Maximum Likelihood.
Jen Ning Lim, Juan Kuntz, Samuel Power, Adam M. Johansen
2024Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments.
Han Wang, Sihong He, Zhili Zhang, Fei Miao, James Anderson
2024Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews.
Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou
2024Monotone Individual Fairness.
Yahav Bechavod
2024Monotone, Bi-Lipschitz, and Polyak-Łojasiewicz Networks.
Ruigang Wang, Krishnamurthy Dj Dvijotham, Ian R. Manchester
2024More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning.
Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
2024More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.
Hossein Zakerinia, Amin Behjati, Christoph H. Lampert
2024Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-Loop and Hessian-Free Solution Strategy.
Risheng Liu, Zhu Liu, Wei Yao, Shangzhi Zeng, Jin Zhang
2024MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation.
Nianzu Yang, Kaipeng Zeng, Haotian Lu, Yexin Wu, Zexin Yuan, Danni Chen, Shengdian Jiang, Jiaxiang Wu, Yimin Wang, Junchi Yan
2024Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy.
Riqiang Gao, Florin-Cristian Ghesu, Simon Arberet, Shahab Basiri, Esa Kuusela, Martin Kraus, Dorin Comaniciu, Ali Kamen
2024Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing.
Amutheezan Sivagnanam, Ava Pettet, Hunter Lee, Ayan Mukhopadhyay, Abhishek Dubey, Aron Laszka
2024Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering.
Haoyu Zhang, Meng Liu, Zixin Liu, Xuemeng Song, Yaowei Wang, Liqiang Nie
2024Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling.
Ruijia Niu, Dongxia Wu, Kai Kim, Yian Ma, Duncan Watson-Parris, Rose Yu
2024Multi-Patch Prediction: Adapting Language Models for Time Series Representation Learning.
Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu
2024Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions.
Weihan Li, Chengrui Li, Yule Wang, Anqi Wu
2024Multi-Sender Persuasion: A Computational Perspective.
Safwan Hossain, Tonghan Wang, Tao Lin, Yiling Chen, David C. Parkes, Haifeng Xu
2024Multi-Source Conformal Inference Under Distribution Shift.
Yi Liu, Alexander Levis, Sharon-Lise T. Normand, Larry Han
2024Multi-Track Message Passing: Tackling Oversmoothing and Oversquashing in Graph Learning via Preventing Heterophily Mixing.
Hongbin Pei, Yu Li, Huiqi Deng, Jingxin Hai, Pinghui Wang, Jie Ma, Jing Tao, Yuheng Xiong, Xiaohong Guan
2024Multi-View Clustering by Inter-cluster Connectivity Guided Reward.
Hao Dai, Yang Liu, Peng Su, Hecheng Cai, Shudong Huang, Jiancheng Lv
2024Multi-View Stochastic Block Models.
Vincent Cohen-Addad, Tommaso d'Orsi, Silvio Lattanzi, Rajai Nasser
2024Multi-group Learning for Hierarchical Groups.
Samuel Deng, Daniel Hsu
2024Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning.
Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
2024MultiMax: Sparse and Multi-Modal Attention Learning.
Yuxuan Zhou, Mario Fritz, Margret Keuper
2024Multicalibration for Confidence Scoring in LLMs.
Gianluca Detommaso, Martin Bertran Lopez, Riccardo Fogliato, Aaron Roth
2024Multigroup Robustness.
Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
2024Multimodal Prototyping for cancer survival prediction.
Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood
2024Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems.
Ta Duy Nguyen, Alina Ene
2024Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains.
Steven Wilkins-Reeves, Xu Chen, Qi Ma, Christine Agarwal, Aude Hofleitner
2024Multiply-Robust Causal Change Attribution.
Victor Quintas-Martinez, Mohammad Taha Bahadori, Eduardo Santiago, Jeff Mu, David Heckerman
2024MusicFlow: Cascaded Flow Matching for Text Guided Music Generation.
K. R. Prajwal, Bowen Shi, Matthew Le, Apoorv Vyas, Andros Tjandra, Mahi Luthra, Baishan Guo, Huiyu Wang, Triantafyllos Afouras, David Kant, Wei-Ning Hsu
2024MusicRL: Aligning Music Generation to Human Preferences.
Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli
2024MuxServe: Flexible Spatial-Temporal Multiplexing for Multiple LLM Serving.
Jiangfei Duan, Runyu Lu, Haojie Duanmu, Xiuhong Li, Xingcheng Zhang, Dahua Lin, Ion Stoica, Hao Zhang
2024NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks.
Haiyan Jiang, Giulia De Masi, Huan Xiong, Bin Gu
2024NExT-Chat: An LMM for Chat, Detection and Segmentation.
Ao Zhang, Yuan Yao, Wei Ji, Zhiyuan Liu, Tat-Seng Chua
2024NExT-GPT: Any-to-Any Multimodal LLM.
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, Tat-Seng Chua
2024NExT: Teaching Large Language Models to Reason about Code Execution.
Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng, Kensen Shi, Charles Sutton, Pengcheng Yin
2024Naive Bayes Classifiers over Missing Data: Decision and Poisoning.
Song Bian, Xiating Ouyang, Zhiwei Fan, Paraschos Koutris
2024Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning.
Joon Suk Huh, Kirthevasan Kandasamy
2024Nash Learning from Human Feedback.
Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mesnard, Côme Fiegel, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
2024NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models.
Zeqian Ju, Yuancheng Wang, Kai Shen, Xu Tan, Detai Xin, Dongchao Yang, Eric Liu, Yichong Leng, Kaitao Song, Siliang Tang, Zhizheng Wu, Tao Qin, Xiangyang Li, Wei Ye, Shikun Zhang, Jiang Bian, Lei He, Jinyu Li, Sheng Zhao
2024Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching.
Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, Yang You
2024Navigating Scaling Laws: Compute Optimality in Adaptive Model Training.
Sotiris Anagnostidis, Gregor Bachmann, Imanol Schlag, Thomas Hofmann
2024NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction.
Haofan Lu, Christopher Vattheuer, Baharan Mirzasoleiman, Omid Abari
2024Near-Linear Time Approximation Algorithms for k-means with Outliers.
Junyu Huang, Qilong Feng, Ziyun Huang, Jinhui Xu, Jianxin Wang
2024Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback.
Asaf Cassel, Haipeng Luo, Aviv Rosenberg, Dmitry Sotnikov
2024Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints.
Dan Qiao, Yu-Xiang Wang
2024Nearest Neighbour Score Estimators for Diffusion Generative Models.
Matthew Niedoba, Dylan Green, Saeid Naderiparizi, Vasileios Lioutas, Jonathan Wilder Lavington, Xiaoxuan Liang, Yunpeng Liu, Ke Zhang, Setareh Dabiri, Adam Scibior, Berend Zwartsenberg, Frank Wood
2024Neighboring Perturbations of Knowledge Editing on Large Language Models.
Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang, Jia-Chen Gu
2024Nesting Particle Filters for Experimental Design in Dynamical Systems.
Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad
2024Network Tight Community Detection.
Jiayi Deng, Xiaodong Yang, Jun Yu, Jun Liu, Zhaiming Shen, Danyang Huang, Huimin Cheng
2024Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction.
Arjun Subramonian, Levent Sagun, Yizhou Sun
2024Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model.
Hien Dang, Tho Tran Huu, Tan Minh Nguyen, Nhat Ho
2024Neural Collapse in Multi-label Learning with Pick-all-label Loss.
Pengyu Li, Xiao Li, Yutong Wang, Qing Qu
2024Neural Collapse meets Differential Privacy: Curious behaviors of NoisyGD with Near-Perfect Representation Learning.
Chendi Wang, Yuqing Zhu, Weijie J. Su, Yu-Xiang Wang
2024Neural Diffusion Models.
Grigory Bartosh, Dmitry P. Vetrov, Christian A. Naesseth
2024Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity.
Hagyeong Lee, Minkyu Kim, Jun-Hyuk Kim, Seungeon Kim, Dokwan Oh, Jaeho Lee
2024Neural Jump-Diffusion Temporal Point Processes.
Shuai Zhang, Chuan Zhou, Yang Aron Liu, Peng Zhang, Xixun Lin, Zhi-Ming Ma
2024Neural NeRF Compression.
Tuan Pham, Stephan Mandt
2024Neural Networks Learn Statistics of Increasing Complexity.
Nora Belrose, Quintin Pope, Lucia Quirke, Alex Mallen, Xiaoli Z. Fern
2024Neural Operators with Localized Integral and Differential Kernels.
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar
2024Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics.
Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams, Johannes Brandstetter
2024Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks.
Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro
2024Neural Tangent Kernels for Axis-Aligned Tree Ensembles.
Ryuichi Kanoh, Mahito Sugiyama
2024Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving.
Alexander Rudikov, Vladimir Fanaskov, Ekaterina A. Muravleva, Yuri M. Laevsky, Ivan V. Oseledets
2024Neural-Kernel Conditional Mean Embeddings.
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
2024NeuralIndicator: Implicit Surface Reconstruction from Neural Indicator Priors.
Shi-Sheng Huang, Guo Chen, Chen Li Heng, Hua Huang
2024Neuro-Symbolic Temporal Point Processes.
Yang Yang, Chao Yang, Boyang Li, Yinghao Fu, Shuang Li
2024Neuro-Visualizer: A Novel Auto-Encoder-Based Loss Landscape Visualization Method With an Application in Knowledge-Guided Machine Learning.
Mohannad Elhamod, Anuj Karpatne
2024Neurodegenerative Brain Network Classification via Adaptive Diffusion with Temporal Regularization.
Hyuna Cho, Jaeyoon Sim, Guorong Wu, Won Hwa Kim
2024Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields.
Tom Fischer, Pascal Peter, Joachim Weickert, Eddy Ilg
2024New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering.
Sanjoy Dasgupta, Eduardo Sany Laber
2024New Sample Complexity Bounds for Sample Average Approximation in Heavy-Tailed Stochastic Programming.
Hongcheng Liu, Jindong Tong
2024No Dimensional Sampling Coresets for Classification.
Meysam Alishahi, Jeff M. Phillips
2024No Double Descent in Principal Component Regression: A High-Dimensional Analysis.
Daniel Gedon, Antônio H. Ribeiro, Thomas B. Schön
2024No Free Prune: Information-Theoretic Barriers to Pruning at Initialization.
Tanishq Kumar, Kevin Luo, Mark Sellke
2024No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths.
Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas
2024No-Regret Reinforcement Learning in Smooth MDPs.
Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restelli
2024Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization.
Kwang-Sung Jun, Jungtaek Kim
2024Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning.
Saber Malekmohammadi, Yaoliang Yu, Yang Cao
2024Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation.
Yudan Wang, Yue Wang, Yi Zhou, Shaofeng Zou
2024Non-Vacuous Generalization Bounds for Large Language Models.
Sanae Lotfi, Marc Anton Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
2024Non-clairvoyant Scheduling with Partial Predictions.
Ziyad Benomar, Vianney Perchet
2024Non-confusing Generation of Customized Concepts in Diffusion Models.
Wang Lin, Jingyuan Chen, Jiaxin Shi, Yichen Zhu, Chen Liang, Junzhong Miao, Tao Jin, Zhou Zhao, Fei Wu, Shuicheng Yan, Hanwang Zhang
2024Non-convex Stochastic Composite Optimization with Polyak Momentum.
Yuan Gao, Anton Rodomanov, Sebastian U. Stich
2024Non-parametric Online Change Point Detection on Riemannian Manifolds.
Xiuheng Wang, Ricardo Augusto Borsoi, Cédric Richard
2024Non-stationary Online Convex Optimization with Arbitrary Delays.
Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang
2024Nonlinear Filtering with Brenier Optimal Transport Maps.
Mohammad Al-Jarrah, Niyizhen Jin, Bamdad Hosseini, Amirhossein Taghvaei
2024Nonparametric Teaching of Implicit Neural Representations.
Chen Zhang, Steven Tin Sui Luo, Jason Chun Lok Li, Yik-Chung Wu, Ngai Wong
2024Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates.
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
2024Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image Generators.
Jianhao Yuan, Francesco Pinto, Adam Davies, Philip Torr
2024Not all distributional shifts are equal: Fine-grained robust conformal inference.
Jiahao Ai, Zhimei Ren
2024Novel Spectral Algorithms for the Partial Credit Model.
Duc Nguyen, Anderson Ye Zhang
2024OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification.
Shikhar Mohan, Deepak Saini, Anshul Mittal, Sayak Ray Chowdhury, Bhawna Paliwal, Jian Jiao, Manish Gupta, Manik Varma
2024ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models.
Dongha Kim, Jaesung Hwang, Jongjin Lee, Kunwoong Kim, Yongdai Kim
2024ODIN: Disentangled Reward Mitigates Hacking in RLHF.
Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro
2024OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning.
Sheng Yue, Xingyuan Hua, Ju Ren, Sen Lin, Junshan Zhang, Yaoxue Zhang
2024OMPO: A Unified Framework for RL under Policy and Dynamics Shifts.
Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang, Huazhe Xu, Xianyuan Zhan
2024OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift.
Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling
2024OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos.
Ziyang Song, Jinxi Li, Bo Yang
2024OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization.
Xiang Meng, Shibal Ibrahim, Kayhan Behdin, Hussein Hazimeh, Natalia Ponomareva, Rahul Mazumder
2024OT-CLIP: Understanding and Generalizing CLIP via Optimal Transport.
Liangliang Shi, Jack Fan, Junchi Yan
2024OTMatch: Improving Semi-Supervised Learning with Optimal Transport.
Zhiquan Tan, Kaipeng Zheng, Weiran Huang
2024Observable Propagation: Uncovering Feature Vectors in Transformers.
Jacob Dunefsky, Arman Cohan
2024Off-policy Evaluation Beyond Overlap: Sharp Partial Identification Under Smoothness.
Samir Khan, Martin Saveski, Johan Ugander
2024Offline Actor-Critic Reinforcement Learning Scales to Large Models.
Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller
2024Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching.
Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
2024Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms.
Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
2024Offline Multi-Objective Optimization.
Ke Xue, Rong-Xi Tan, Xiaobin Huang, Chao Qian
2024Offline Training of Language Model Agents with Functions as Learnable Weights.
Shaokun Zhang, Jieyu Zhang, Jiale Liu, Linxin Song, Chi Wang, Ranjay Krishna, Qingyun Wu
2024Offline Transition Modeling via Contrastive Energy Learning.
Ruifeng Chen, Chengxing Jia, Zefang Huang, Tian-Shuo Liu, Xu-Hui Liu, Yang Yu
2024Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL.
Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang, Huazhe Xu, Xianyuan Zhan
2024On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis.
Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, Han Liu
2024On Convergence of Incremental Gradient for Non-convex Smooth Functions.
Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi
2024On Discrete Prompt Optimization for Diffusion Models.
Ruochen Wang, Ting Liu, Cho-Jui Hsieh, Boqing Gong
2024On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box.
Yi Cai, Gerhard Wunder
2024On Hypothesis Transfer Learning of Functional Linear Models.
Haotian Lin, Matthew Reimherr
2024On Interpolating Experts and Multi-Armed Bandits.
Houshuang Chen, Yuchen He, Chihao Zhang
2024On Learning Deep O(n)-Equivariant Hyperspheres.
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Andreas Robinson, Cuong Le
2024On Least Square Estimation in Softmax Gating Mixture of Experts.
Huy Nguyen, Nhat Ho, Alessandro Rinaldo
2024On Mechanistic Knowledge Localization in Text-to-Image Generative Models.
Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda, Vlad I. Morariu, Nanxuan Zhao, Ryan A. Rossi, Varun Manjunatha, Soheil Feizi
2024On Multi-Armed Bandit with Impatient Arms.
Yuming Shao, Zhixuan Fang
2024On Online Experimentation without Device Identifiers.
Shiv Shankar, Ritwik Sinha, Madalina Fiterau
2024On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization.
Motahareh Sohrabi, Juan Ramirez, Tianyue H. Zhang, Simon Lacoste-Julien, Jose Gallego-Posada
2024On Positivity Condition for Causal Inference.
Inwoo Hwang, Yesong Choe, Yeahoon Kwon, Sanghack Lee
2024On Prompt-Driven Safeguarding for Large Language Models.
Chujie Zheng, Fan Yin, Hao Zhou, Fandong Meng, Jie Zhou, Kai-Wei Chang, Minlie Huang, Nanyun Peng
2024On Statistical Learning Theory for Distributional Inputs.
Christian Fiedler, Pierre-François Massiani, Friedrich Solowjow, Sebastian Trimpe
2024On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning.
Ari Karchmer
2024On The Complexity of First-Order Methods in Stochastic Bilevel Optimization.
Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu
2024On The Fairness Impacts of Hardware Selection in Machine Learning.
Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, Ferdinando Fioretto
2024On The Statistical Complexity of Offline Decision-Making.
Thanh Nguyen-Tang, Raman Arora
2024On Universally Optimal Algorithms for A/B Testing.
Po-An Wang, Kaito Ariu, Alexandre Proutière
2024On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective.
Jincheng Huang, Jialie Shen, Xiaoshuang Shi, Xiaofeng Zhu
2024On a Combinatorial Problem Arising in Machine Teaching.
Joakim Sunde, Brigt Arve Toppe Håvardstun, Jan Kratochvíl, Jan Arne Telle
2024On a Neural Implementation of Brenier's Polar Factorization.
Nina Vesseron, Marco Cuturi
2024On dimensionality of feature vectors in MPNNs.
César Bravo, Alexander Kozachinskiy, Cristobal Rojas
2024On the Asymptotic Distribution of the Minimum Empirical Risk.
Jacob Westerhout, TrungTin Nguyen, Xin Guo, Hien Duy Nguyen
2024On the Calibration of Human Pose Estimation.
Kerui Gu, Rongyu Chen, Xuanlong Yu, Angela Yao
2024On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition.
Yunyan Bai, Yuxing Liu, Luo Luo
2024On the Consistency of Kernel Methods with Dependent Observations.
Pierre-François Massiani, Sebastian Trimpe, Friedrich Solowjow
2024On the Convergence of Projected Bures-Wasserstein Gradient Descent under Euclidean Strong Convexity.
Junyi Fan, Yuxuan Han, Zijian Liu, Jian-Feng Cai, Yang Wang, Zhengyuan Zhou
2024On the Diminishing Returns of Width for Continual Learning.
Etash Kumar Guha, Vihan Lakshman
2024On the Duality Between Sharpness-Aware Minimization and Adversarial Training.
Yihao Zhang, Hangzhou He, Jingyu Zhu, Huanran Chen, Yifei Wang, Zeming Wei
2024On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning.
Jeongheon Oh, Kibok Lee
2024On the Embedding Collapse when Scaling up Recommendation Models.
Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long
2024On the Emergence of Cross-Task Linearity in Pretraining-Finetuning Paradigm.
Zhanpeng Zhou, Zijun Chen, Yilan Chen, Bo Zhang, Junchi Yan
2024On the Error-Propagation of Inexact Hotelling's Deflation for Principal Component Analysis.
Fangshuo Liao, Junhyung Lyle Kim, Cruz Barnum, Anastasios Kyrillidis
2024On the Expressive Power of Spectral Invariant Graph Neural Networks.
Bohang Zhang, Lingxiao Zhao, Haggai Maron
2024On the Feasibility of Single-Pass Full-Capacity Learning in Linear Threshold Neurons with Binary Input Vectors.
Ruipeng Liu, Borui He, Naveed Tahir, Garrett E. Katz
2024On the Generalization of Equivariant Graph Neural Networks.
Rafal Karczewski, Amauri H. Souza, Vikas Garg
2024On the Hardness of Probabilistic Neurosymbolic Learning.
Jaron Maene, Vincent Derkinderen, Luc De Raedt
2024On the Identifiability of Switching Dynamical Systems.
Carles Balsells Rodas, Yixin Wang, Yingzhen Li
2024On the Implicit Bias of Adam.
Matias D. Cattaneo, Jason M. Klusowski, Boris Shigida
2024On the Independence Assumption in Neurosymbolic Learning.
Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari
2024On the Last-Iterate Convergence of Shuffling Gradient Methods.
Zijian Liu, Zhengyuan Zhou
2024On the Maximal Local Disparity of Fairness-Aware Classifiers.
Jinqiu Jin, Haoxuan Li, Fuli Feng
2024On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions.
Denys Pushkin, Raphaël Berthier, Emmanuel Abbe
2024On the Nonlinearity of Layer Normalization.
Yunhao Ni, Yuxin Guo, Junlong Jia, Lei Huang
2024On the Origins of Linear Representations in Large Language Models.
Yibo Jiang, Goutham Rajendran, Pradeep Kumar Ravikumar, Bryon Aragam, Victor Veitch
2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data.
Shunxing Fan, Mingming Gong, Kun Zhang
2024On the Role of Edge Dependency in Graph Generative Models.
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos E. Tsourakakis
2024On the Second-Order Convergence of Biased Policy Gradient Algorithms.
Siqiao Mu, Diego Klabjan
2024On the Tractability of SHAP Explanations under Markovian Distributions.
Reda Marzouk, Colin de la Higuera
2024On the Trajectory Regularity of ODE-based Diffusion Sampling.
Defang Chen, Zhenyu Zhou, Can Wang, Chunhua Shen, Siwei Lyu
2024On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation.
Alvaro Labarca, Denis Parra, Rodrigo Toro Icarte
2024On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows.
Felix Draxler, Stefan Wahl, Christoph Schnörr, Ullrich Köthe
2024On the Weight Dynamics of Deep Normalized Networks.
Christian H. X. Ali Mehmeti-Göpel, Michael Wand
2024On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery.
Fateme Jamshidi, Luca Ganassali, Negar Kiyavash
2024One Meta-tuned Transformer is What You Need for Few-shot Learning.
Xu Yang, Huaxiu Yao, Ying Wei
2024One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts.
Ruochen Wang, Sohyun An, Minhao Cheng, Tianyi Zhou, Sung Ju Hwang, Cho-Jui Hsieh
2024One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning.
Doyoung Kim, Susik Yoon, Dongmin Park, Youngjun Lee, Hwanjun Song, Jihwan Bang, Jae-Gil Lee
2024One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment.
Chaochao Chen, Jiaming Zhang, Yuyuan Li, Zhongxuan Han
2024One-Shot Strategic Classification Under Unknown Costs.
Elan Rosenfeld, Nir Rosenfeld
2024Online Adaptive Anomaly Thresholding with Confidence Sequences.
Sophia Huiwen Sun, Abishek Sankararaman, Balakrishnan Narayanaswamy
2024Online Algorithms with Uncertainty-Quantified Predictions.
Bo Sun, Jerry Huang, Nicolas Christianson, Mohammad Hajiesmaili, Adam Wierman, Raouf Boutaba
2024Online Cascade Learning for Efficient Inference over Streams.
Lunyiu Nie, Zhimin Ding, Erdong Hu, Christopher M. Jermaine, Swarat Chaudhuri
2024Online Isolation Forest.
Filippo Leveni, Guilherme Weigert Cassales, Bernhard Pfahringer, Albert Bifet, Giacomo Boracchi
2024Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs.
Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan
2024Online Learning in Betting Markets: Profit versus Prediction.
Haiqing Zhu, Alexander Soen, Yun Kuen Cheung, Lexing Xie
2024Online Learning in CMDPs: Handling Stochastic and Adversarial Constraints.
Francesco Emanuele Stradi, Jacopo Germano, Gianmarco Genalti, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
2024Online Learning under Budget and ROI Constraints via Weak Adaptivity.
Matteo Castiglioni, Andrea Celli, Christian Kroer
2024Online Learning with Bounded Recall.
Jon Schneider, Kiran Vodrahalli
2024Online Linear Regression in Dynamic Environments via Discounting.
Andrew Jacobsen, Ashok Cutkosky
2024Online Matching with Stochastic Rewards: Provable Better Bound via Adversarial Reinforcement Learning.
Qiankun Zhang, Aocheng Shen, Boyu Zhang, Hanrui Jiang, Bingqian Du
2024Online Matrix Completion: A Collaborative Approach with Hott Items.
Dheeraj Baby, Soumyabrata Pal
2024Online Resource Allocation with Non-Stationary Customers.
Xiaoyue Zhang, Hanzhang Qin, Mabel C. Chou
2024Online Speculative Decoding.
Xiaoxuan Liu, Lanxiang Hu, Peter Bailis, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang
2024Online Variational Sequential Monte Carlo.
Alessandro Mastrototaro, Jimmy Olsson
2024Online bipartite matching with imperfect advice.
Davin Choo, Themistoklis Gouleakis, Chun Kai Ling, Arnab Bhattacharyya
2024Online conformal prediction with decaying step sizes.
Anastasios Nikolas Angelopoulos, Rina Barber, Stephen Bates
2024Open Ad Hoc Teamwork with Cooperative Game Theory.
Jianhong Wang, Yang Li, Yuan Zhang, Wei Pan, Samuel Kaski
2024Open-Domain Text Evaluation via Contrastive Distribution Methods.
Sidi Lu, Hongyi Liu, Asli Celikyilmaz, Tianlu Wang, Nanyun Peng
2024Open-Vocabulary Calibration for Fine-tuned CLIP.
Shuoyuan Wang, Jindong Wang, Guoqing Wang, Bob Zhang, Kaiyang Zhou, Hongxin Wei
2024OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models.
Fuzhao Xue, Zian Zheng, Yao Fu, Jinjie Ni, Zangwei Zheng, Wangchunshu Zhou, Yang You
2024Operator SVD with Neural Networks via Nested Low-Rank Approximation.
Jongha Jon Ryu, Xiangxiang Xu, Hasan Sabri Melihcan Erol, Yuheng Bu, Lizhong Zheng, Gregory W. Wornell
2024OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models.
Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell
2024Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique.
Taeho Yoon, Jaeyeon Kim, Jaewook J. Suh, Ernest K. Ryu
2024Optimal Batched Linear Bandits.
Xuanfei Ren, Tianyuan Jin, Pan Xu
2024Optimal Coresets for Low-Dimensional Geometric Median.
Peyman Afshani, Chris Schwiegelshohn
2024Optimal Differentially Private Model Training with Public Data.
Andrew Lowy, Zeman Li, Tianjian Huang, Meisam Razaviyayn
2024Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks.
Haixiao Wang, Zhichao Wang
2024Optimal Eye Surgeon: Finding image priors through sparse generators at initialization.
Avrajit Ghosh, Xitong Zhang, Kenneth K. Sun, Qing Qu, Saiprasad Ravishankar, Rongrong Wang
2024Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization.
Feihu Huang
2024Optimal Kernel Choice for Score Function-based Causal Discovery.
Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong
2024Optimal Kernel Quantile Learning with Random Features.
Caixing Wang, Xingdong Feng
2024Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction.
Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess, Daniel Durstewitz
2024Optimal Ridge Regularization for Out-of-Distribution Prediction.
Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
2024Optimal Transport for Structure Learning Under Missing Data.
Vy Vo, He Zhao, Trung Le, Edwin V. Bonilla, Dinh Phung
2024Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation.
Alexander Munteanu, Simon Omlor
2024Optimally Improving Cooperative Learning in a Social Setting.
Shahrzad Haddadan, Cheng Xin, Jie Gao
2024Optimistic Multi-Agent Policy Gradient.
Wenshuai Zhao, Yi Zhao, Zhiyuan Li, Juho Kannala, Joni Pajarinen
2024Optimization without Retraction on the Random Generalized Stiefel Manifold.
Simon Vary, Pierre Ablin, Bin Gao, Pierre-Antoine Absil
2024Optimizing Watermarks for Large Language Models.
Bram Wouters
2024Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty.
Kaizhao Liu, José H. Blanchet, Lexing Ying, Yiping Lu
2024Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift.
Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel
2024Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble.
Chenhui Xu, Fuxun Yu, Zirui Xu, Nathan Inkawhich, Xiang Chen
2024Out-of-Domain Generalization in Dynamical Systems Reconstruction.
Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz
2024Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.
Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Gen Li, Ajay Kumar Jaiswal, Mykola Pechenizkiy, Yi Liang, Michael Bendersky, Zhangyang Wang, Shiwei Liu
2024Outlier-Efficient Hopfield Layers for Large Transformer-Based Models.
Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Haozheng Luo, Hong-Yu Chen, Weijian Li, Wei-Po Wang, Han Liu
2024Outlier-aware Slicing for Post-Training Quantization in Vision Transformer.
Yuexiao Ma, Huixia Li, Xiawu Zheng, Feng Ling, Xuefeng Xiao, Rui Wang, Shilei Wen, Fei Chao, Rongrong Ji
2024Outlier-robust Kalman Filtering through Generalised Bayes.
Gerardo Duran-Martin, Matías Altamirano, Alexander Y. Shestopaloff, Leandro Sánchez-Betancourt, Jeremias Knoblauch, Matt Jones, François-Xavier Briol, Kevin Patrick Murphy
2024Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors.
Chun-Yin Huang, Kartik Srinivas, Xin Zhang, Xiaoxiao Li
2024Overcoming Saturation in Density Ratio Estimation by Iterated Regularization.
Lukas Gruber, Markus Holzleitner, Johannes Lehner, Sepp Hochreiter, Werner Zellinger
2024Overcoming the Optimizer's Curse: Obtaining Realistic Prescriptions from Neural Networks.
Asterios Tsiourvas, Georgia Perakis
2024Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning.
Michal Nauman, Michal Bortkiewicz, Piotr Milos, Tomasz Trzcinski, Mateusz Ostaszewski, Marek Cygan
2024OxyGenerator: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning.
Bin Lu, Ze Zhao, Luyu Han, Xiaoying Gan, Yuntao Zhou, Lei Zhou, Luoyi Fu, Xinbing Wang, Chenghu Zhou, Jing Zhang
2024PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models.
Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Mihály Petreczky
2024PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning.
Jaejun Lee, Minsung Hwang, Joyce Jiyoung Whang
2024PAGER: Accurate Failure Characterization in Deep Regression Models.
Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Puja Trivedi, Rushil Anirudh
2024PANDA: Expanded Width-Aware Message Passing Beyond Rewiring.
Jeongwhan Choi, Sumin Park, Hyowon Wi, Sung-Bae Cho, Noseong Park
2024PAPM: A Physics-aware Proxy Model for Process Systems.
Pengwei Liu, Zhongkai Hao, Xingyu Ren, Hangjie Yuan, Jiayang Ren, Dong Ni
2024PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling.
Phong C. H. Nguyen, Xinlun Cheng, Shahab Azarfar, Pradeep K. Seshadri, Yen Thi Nguyen, Munho Kim, Sanghun Choi, H. S. Udaykumar, Stephen Baek
2024PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition.
Ziyang Zhang, Qizhen Zhang, Jakob Nicolaus Foerster
2024PASOA- PArticle baSed Bayesian Optimal Adaptive design.
Jacopo Iollo, Christophe Heinkelé, Pierre Alliez, Florence Forbes
2024PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming.
Bingheng Li, Linxin Yang, Yupeng Chen, Senmiao Wang, Haitao Mao, Qian Chen, Yao Ma, Akang Wang, Tian Ding, Jiliang Tang, Ruoyu Sun
2024PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation.
Runze Liu, Yali Du, Fengshuo Bai, Jiafei Lyu, Xiu Li
2024PGODE: Towards High-quality System Dynamics Modeling.
Xiao Luo, Yiyang Gu, Huiyu Jiang, Hang Zhou, Jinsheng Huang, Wei Ju, Zhiping Xiao, Ming Zhang, Yizhou Sun
2024PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning.
Hyeong Kyu Choi, Yixuan Li
2024PID: Prompt-Independent Data Protection Against Latent Diffusion Models.
Ang Li, Yichuan Mo, Mingjie Li, Yisen Wang
2024PIDformer: Transformer Meets Control Theory.
Tam Minh Nguyen, César A. Uribe, Tan Minh Nguyen, Richard G. Baraniuk
2024PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling.
Utsav Singh, Wesley A. Suttle, Brian M. Sadler, Vinay P. Namboodiri, Amrit S. Bedi
2024PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs.
Soroush Nasiriany, Fei Xia, Wenhao Yu, Ted Xiao, Jacky Liang, Ishita Dasgupta, Annie Xie, Danny Driess, Ayzaan Wahid, Zhuo Xu, Quan Vuong, Tingnan Zhang, Tsang-Wei Edward Lee, Kuang-Huei Lee, Peng Xu, Sean Kirmani, Yuke Zhu, Andy Zeng, Karol Hausman, Nicolas Heess, Chelsea Finn, Sergey Levine, Brian Ichter
2024PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching.
Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li
2024PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control.
Ruijie Zheng, Ching-An Cheng, Hal Daumé III, Furong Huang, Andrey Kolobov
2024PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect.
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi
2024Pairwise Alignment Improves Graph Domain Adaptation.
Shikun Liu, Deyu Zou, Han Zhao, Pan Li
2024Parallel Affine Transformation Tuning of Markov Chain Monte Carlo.
Philip Schär, Michael Habeck, Daniel Rudolf
2024Parallelized Spatiotemporal Slot Binding for Videos.
Gautam Singh, Yue Wang, Jiawei Yang, Boris Ivanovic, Sungjin Ahn, Marco Pavone, Tong Che
2024Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation.
Xinyu Ma, Xu Chu, Zhibang Yang, Yang Lin, Xin Gao, Junfeng Zhao
2024Parameter Estimation in DAGs from Incomplete Data via Optimal Transport.
Vy Vo, Trung Le, Long Tung Vuong, He Zhao, Edwin V. Bonilla, Dinh Phung
2024Parameter-Dependent Competitive Analysis for Online Capacitated Coverage Maximization through Boostings and Attenuations.
Pan Xu
2024Parameter-Efficient Fine-Tuning with Controls.
Chi Zhang, Jingpu Cheng, Yanyu Xu, Qianxiao Li
2024Parameter-Efficient Fine-Tuning with Discrete Fourier Transform.
Ziqi Gao, Qichao Wang, Aochuan Chen, Zijing Liu, Bingzhe Wu, Liang Chen, Jia Li
2024Parameterized Physics-informed Neural Networks for Parameterized PDEs.
Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park
2024Parsimonious Learning-Augmented Approximations for Dense Instances of NP-hard Problems.
Evripidis Bampis, Bruno Escoffier, Michalis Xefteris
2024Partial Multi-View Multi-Label Classification via Semantic Invariance Learning and Prototype Modeling.
Chengliang Liu, Gehui Xu, Jie Wen, Yabo Liu, Chao Huang, Yong Xu
2024Partial Optimality in the Linear Ordering Problem.
David Stein, Bjoern Andres
2024Partially Stochastic Infinitely Deep Bayesian Neural Networks.
Sergio Calvo-Ordoñez, Matthieu Meunier, Francesco Piatti, Yuantao Shi
2024Particle Denoising Diffusion Sampler.
Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet
2024Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models.
Asma Ghandeharioun, Avi Caciularu, Adam Pearce, Lucas Dixon, Mor Geva
2024Path-Guided Particle-based Sampling.
Mingzhou Fan, Ruida Zhou, Chao Tian, Xiaoning Qian
2024Pausing Policy Learning in Non-stationary Reinforcement Learning.
Hyunin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi
2024PcLast: Discovering Plannable Continuous Latent States.
Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan P. Molu, Miroslav Dudík, John Langford, Alex Lamb
2024Pedestrian Attribute Recognition as Label-balanced Multi-label Learning.
Yibo Zhou, Hai-Miao Hu, Yirong Xiang, Xiaokang Zhang, Haotian Wu
2024Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams.
Brian Cho, Kyra Gan, Nathan Kallus
2024PerceptAnon: Exploring the Human Perception of Image Anonymization Beyond Pseudonymization for GDPR.
Kartik Patwari, Chen-Nee Chuah, Lingjuan Lyu, Vivek Sharma
2024Perfect Alignment May be Poisonous to Graph Contrastive Learning.
Jingyu Liu, Huayi Tang, Yong Liu
2024Performance Bounds for Active Binary Testing with Information Maximization.
Aditya Chattopadhyay, Benjamin David Haeffele, René Vidal, Donald Geman
2024Performative Prediction with Bandit Feedback: Learning through Reparameterization.
Yatong Chen, Wei Tang, Chien-Ju Ho, Yang Liu
2024Perturb-and-Project: Differentially Private Similarities and Marginals.
Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong
2024Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning.
Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang
2024Physics and Lie symmetry informed Gaussian processes.
David Dalton, Dirk Husmeier, Hao Gao
2024Physics of Language Models: Part 3.1, Knowledge Storage and Extraction.
Zeyuan Allen-Zhu, Yuanzhi Li
2024Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification.
Yiming Meng, Ruikun Zhou, Amartya Mukherjee, Maxwell Fitzsimmons, Christopher Song, Jun Liu
2024Pi-DUAL: Using privileged information to distinguish clean from noisy labels.
Ke Wang, Guillermo Ortiz-Jiménez, Rodolphe Jenatton, Mark Collier, Efi Kokiopoulou, Pascal Frossard
2024Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach.
Mim van den Bos, Jacobus G. M. van der Linden, Emir Demirovic
2024PinNet: Pinpoint Instructive Information for Retrieval Augmented Code-to-Text Generation.
Han Fu, Jian Tan, Pinhan Zhang, Feifei Li, Jianling Sun
2024PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer.
Chang Chen, Junyeob Baek, Fei Deng, Kenji Kawaguchi, Caglar Gulcehre, Sungjin Ahn
2024Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners.
Chengjie Wu, Hao Hu, Yiqin Yang, Ning Zhang, Chongjie Zhang
2024Plug-and-Play image restoration with Stochastic deNOising REgularization.
Marien Renaud, Jean Prost, Arthur Leclaire, Nicolas Papadakis
2024Plug-in Performative Optimization.
Licong Lin, Tijana Zrnic
2024Pluvial Flood Emulation with Hydraulics-informed Message Passing.
Arnold Kazadi, James Doss-Gollin, Arlei Lopes da Silva
2024PointMC: Multi-instance Point Cloud Registration based on Maximal Cliques.
Yue Wu, Xidao Hu, Yongzhe Yuan, Xiaolong Fan, Maoguo Gong, Hao Li, Mingyang Zhang, Qiguang Miao, Wenping Ma
2024Policy Evaluation for Variance in Average Reward Reinforcement Learning.
Shubhada Agrawal, Prashanth L. A., Siva Theja Maguluri
2024Policy Learning for Balancing Short-Term and Long-Term Rewards.
Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng
2024Policy-conditioned Environment Models are More Generalizable.
Ruifeng Chen, Xiong-Hui Chen, Yihao Sun, Siyuan Xiao, Minhui Li, Yang Yu
2024PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels.
Praneeth Kacham, Vahab Mirrokni, Peilin Zhong
2024Polynomial-based Self-Attention for Table Representation Learning.
Jayoung Kim, Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park
2024Position: A Call for Embodied AI.
Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
2024Position: A Call to Action for a Human-Centered AutoML Paradigm.
Marius Lindauer, Florian Karl, Anne Klier, Julia Moosbauer, Alexander Tornede, Andreas Müller, Frank Hutter, Matthias Feurer, Bernd Bischl
2024Position: A Roadmap to Pluralistic Alignment.
Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L. Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi
2024Position: A Safe Harbor for AI Evaluation and Red Teaming.
Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson
2024Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research.
Riley Simmons-Edler, Ryan Paul Badman, Shayne Longpre, Kanaka Rajan
2024Position: AI/ML Influencers Have a Place in the Academic Process.
Iain Weissburg, Mehir Arora, Xinyi Wang, Liangming Pan, William Yang Wang
2024Position: Amazing Things Come From Having Many Good Models.
Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
2024Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience.
Martina G. Vilas, Federico Adolfi, David Poeppel, Gemma Roig
2024Position: Application-Driven Innovation in Machine Learning.
David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
2024Position: Automatic Environment Shaping is the Next Frontier in RL.
Younghyo Park, Gabriel B. Margolis, Pulkit Agrawal
2024Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024Position: Benchmarking is Limited in Reinforcement Learning Research.
Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas
2024Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis.
Jessica Dai
2024Position: Building Guardrails for Large Language Models Requires Systematic Design.
Yi Dong, Ronghui Mu, Gaojie Jin, Yi Qi, Jinwei Hu, Xingyu Zhao, Jie Meng, Wenjie Ruan, Xiaowei Huang
2024Position: Categorical Deep Learning is an Algebraic Theory of All Architectures.
Bruno Gavranovic, Paul Lessard, Andrew Joseph Dudzik, Tamara von Glehn, João Guilherme Madeira Araújo, Petar Velickovic
2024Position: Compositional Generative Modeling: A Single Model is Not All You Need.
Yilun Du, Leslie Pack Kaelbling
2024Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining.
Florian Tramèr, Gautam Kamath, Nicholas Carlini
2024Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities.
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
2024Position: C∗-Algebraic Machine Learning - Moving in a New Direction.
Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
2024Position: Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
Shayne Longpre, Robert Mahari, Naana Obeng-Marnu, William Brannon, Tobin South, Katy Ilonka Gero, Alex Pentland, Jad Kabbara
2024Position: Data-driven Discovery with Large Generative Models.
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Sanchaita Hazra, Ashish Sabharwal, Peter Clark
2024Position: Do Not Explain Vision Models Without Context.
Paulina Tomaszewska, Przemyslaw Biecek
2024Position: Do pretrained Transformers Learn In-Context by Gradient Descent?
Lingfeng Shen, Aayush Mishra, Daniel Khashabi
2024Position: Embracing Negative Results in Machine Learning.
Florian Karl, Lukas Malte Kemeter, Gabriel Dax, Paulina Sierak
2024Position: Enforced Amnesia as a Way to Mitigate the Potential Risk of Silent Suffering in the Conscious AI.
Yegor Tkachenko
2024Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation.
Shiyang Lai, Yujin Potter, Junsol Kim, Richard Zhuang, Dawn Song, James Evans
2024Position: Explain to Question not to Justify.
Przemyslaw Biecek, Wojciech Samek
2024Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training.
Lin Lu, Chenxi Dai, Wangcheng Tao, Binhang Yuan, Yanan Sun, Pan Zhou
2024Position: Foundation Agents as the Paradigm Shift for Decision Making.
Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao, Junge Zhang
2024Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches.
David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan
2024Position: Future Directions in the Theory of Graph Machine Learning.
Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, Stefanie Jegelka
2024Position: Graph Foundation Models Are Already Here.
Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang
2024Position: Insights from Survey Methodology can Improve Training Data.
Stephanie Eckman, Barbara Plank, Frauke Kreuter
2024Position: Intent-aligned AI Systems Must Optimize for Agency Preservation.
Catalin Mitelut, Benjamin J. Smith, Peter Vamplew
2024Position: Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
2024Position: Key Claims in LLM Research Have a Long Tail of Footnotes.
Anna Rogers, Sasha Luccioni
2024Position: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks.
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Saldyt, Anil Murthy
2024Position: Levels of AGI for Operationalizing Progress on the Path to AGI.
Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clément Farabet, Shane Legg
2024Position: Leverage Foundational Models for Black-Box Optimization.
Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang, Yutian Chen
2024Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms.
Eleonora Bonel, Luca Nannini, Davide Bassi, Michele Joshua Maggini
2024Position: Measure Dataset Diversity, Don't Just Claim It.
Dora Zhao, Jerone T. A. Andrews, Orestis Papakyriakopoulos, Alice Xiang
2024Position: Mission Critical - Satellite Data is a Distinct Modality in Machine Learning.
Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner
2024Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI.
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schröder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Thomas Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob N. Foerster
2024Position: On the Possibilities of AI-Generated Text Detection.
Souradip Chakraborty, Amrit S. Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, Furong Huang
2024Position: On the Societal Impact of Open Foundation Models.
Sayash Kapoor, Rishi Bommasani, Kevin Klyman, Shayne Longpre, Ashwin Ramaswami, Peter Cihon, Aspen K. Hopkins, Kevin Bankston, Stella Biderman, Miranda Bogen, Rumman Chowdhury, Alex Engler, Peter Henderson, Yacine Jernite, Seth Lazar, Stefano Maffulli, Alondra Nelson, Joelle Pineau, Aviya Skowron, Dawn Song, Victor Storchan, Daniel Zhang, Daniel E. Ho, Percy Liang, Arvind Narayanan
2024Position: Open-Endedness is Essential for Artificial Superhuman Intelligence.
Edward Hughes, Michael D. Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel
2024Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy.
Lucas Spangher, Allen M. Wang, Andrew Maris, Myles Stapelberg, Viraj Mehta, Alex Saperstein, Stephen Lane-Walsh, Akshata Kishore Moharir, Alessandro Pau, Cristina Rea
2024Position: Optimization in SciML Should Employ the Function Space Geometry.
Johannes Müller, Marius Zeinhofer
2024Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
M. Saquib Sarfraz, Mei-Yen Chen, Lukas Layer, Kunyu Peng, Marios Koulakis
2024Position: Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination.
Zhiyao Luo, Yangchen Pan, Peter J. Watkinson, Tingting Zhu
2024Position: Relational Deep Learning - Graph Representation Learning on Relational Databases.
Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec
2024Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems.
Yifan Xia, Xianliang Yang, Zichuan Liu, Zhihao Liu, Lei Song, Jiang Bian
2024Position: Scaling Simulation is Neither Necessary Nor Sufficient for In-the-Wild Robot Manipulation.
Homanga Bharadhwaj
2024Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized.
Shomik Jain, Kathleen Creel, Ashia Camage Wilson
2024Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback.
Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs, Nathan Lambert, Milan Mossé, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, Emanuel Tewolde, William S. Zwicker
2024Position: Social Environment Design Should be Further Developed for AI-based Policy-Making.
Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, Yiling Chen
2024Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI.
Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin, Jenny Lee, Yacine Jernite, Carlos Muñoz Ferrandis, Aaron Gokaslan, Alek Tarkowski, Joseph Lindley, A. Feder Cooper, Danish Contractor
2024Position: Stop Making Unscientific AGI Performance Claims.
Patrick Altmeyer, Andrew M. Demetriou, Antony Bartlett, Cynthia C. S. Liem
2024Position: Technical Research and Talent is Needed for Effective AI Governance.
Anka Reuel, Lisa Soder, Benjamin Bucknall, Trond Arne Undheim
2024Position: Tensor Networks are a Valuable Asset for Green AI.
Eva Memmel, Clara Menzen, Jetze Schuurmans, Frederiek Wesel, Kim Batselier
2024Position: The Causal Revolution Needs Scientific Pragmatism.
Joshua R. Loftus
2024Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning.
Micah Goldblum, Marc Anton Finzi, Keefer Rowan, Andrew Gordon Wilson
2024Position: The Platonic Representation Hypothesis.
Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola
2024Position: The Reasonable Person Standard for AI.
Sunayana Rane
2024Position: Topological Deep Learning is the New Frontier for Relational Learning.
Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi
2024Position: Towards Implicit Prompt For Text-To-Image Models.
Yue Yang, Yuqi Lin, Hong Liu, Wenqi Shao, Runjian Chen, Hailong Shang, Yu Wang, Yu Qiao, Kaipeng Zhang, Ping Luo
2024Position: Towards Unified Alignment Between Agents, Humans, and Environment.
Zonghan Yang, An Liu, Zijun Liu, Kaiming Liu, Fangzhou Xiong, Yile Wang, Zeyuan Yang, Qingyuan Hu, Xinrui Chen, Zhenhe Zhang, Fuwen Luo, Zhicheng Guo, Peng Li, Yang Liu
2024Position: TrustLLM: Trustworthiness in Large Language Models.
Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao
2024Position: Understanding LLMs Requires More Than Statistical Generalization.
Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár
2024Position: Video as the New Language for Real-World Decision Making.
Sherry Yang, Jacob C. Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, André Barreto, Pieter Abbeel, Dale Schuurmans
2024Position: What Can Large Language Models Tell Us about Time Series Analysis.
Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen
2024Position: What makes an image realistic?
Lucas Theis
2024Position: Why Tabular Foundation Models Should Be a Research Priority.
Boris van Breugel, Mihaela van der Schaar
2024Position: Why We Must Rethink Empirical Research in Machine Learning.
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
2024Position: Will we run out of data? Limits of LLM scaling based on human-generated data.
Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Marius Hobbhahn
2024Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning.
Junfeng Chen, Kailiang Wu
2024Positive Concave Deep Equilibrium Models.
Mateusz Gabor, Tomasz Piotrowski, Renato L. G. Cavalcante
2024Positive and Unlabeled Learning with Controlled Probability Boundary Fence.
Changchun Li, Yuanchao Dai, Lei Feng, Ximing Li, Bing Wang, Jihong Ouyang
2024Post-hoc Part-Prototype Networks.
Andong Tan, Fengtao Zhou, Hao Chen
2024Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds.
Shion Takeno, Yu Inatsu, Masayuki Karasuyama, Ichiro Takeuchi
2024Potential Based Diffusion Motion Planning.
Yunhao Luo, Chen Sun, Joshua B. Tenenbaum, Yilun Du
2024PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs.
Charlie Hou, Akshat Shrivastava, Hongyuan Zhan, Rylan Conway, Trang Le, Adithya Sagar, Giulia Fanti, Daniel Lazar
2024Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory.
Kai Xu, Hong Ge
2024Practical Performance Guarantees for Pipelined DNN Inference.
Aaron Archer, Matthew Fahrbach, Kuikui Liu, Prakash Prabhu
2024Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input.
Andi Peng, Yuying Sun, Tianmin Shu, David Abel
2024Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains.
Jiale Zhao, Wanru Zhuang, Jia Song, Yaqi Li, Shuqi Lu
2024Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms.
Elvis Dohmatob, Meyer Scetbon
2024Predicting Dose-Response Curves with Deep Neural Networks.
Pedro Alonso Campana, Paul Prasse, Tobias Scheffer
2024Predicting Lagrangian Multipliers for Mixed Integer Linear Programs.
Francesco Demelas, Joseph Le Roux, Mathieu Lacroix, Axel Parmentier
2024Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks.
Haoyu Li, Shichang Zhang, Longwen Tang, Mathieu Bauchy, Yizhou Sun
2024Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg.
Yi Feng, Georgios Piliouras, Xiao Wang
2024Prediction-powered Generalization of Causal Inferences.
Ilker Demirel, Ahmed M. Alaa, Anthony Philippakis, David A. Sontag
2024Predictive Coding beyond Correlations.
Tommaso Salvatori, Luca Pinchetti, Amine M'Charrak, Beren Millidge, Thomas Lukasiewicz
2024Predictive Dynamic Fusion.
Bing Cao, Yinan Xia, Yi Ding, Changqing Zhang, Qinghua Hu
2024Predictive Linear Online Tracking for Unknown Targets.
Anastasios Tsiamis, Aren Karapetyan, Yueshan Li, Efe C. Balta, John Lygeros
2024Predictive Performance Comparison of Decision Policies Under Confounding.
Luke Guerdan, Amanda Coston, Ken Holstein, Steven Wu
2024Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data.
Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar
2024Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models.
Songtao Liu, Hanjun Dai, Yue Zhao, Peng Liu
2024Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss.
Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Shuang Ma, Hal Daumé III, Huazhe Xu, John Langford, Praveen Palanisamy, Kalyan Shankar Basu, Furong Huang
2024Premise Order Matters in Reasoning with Large Language Models.
Xinyun Chen, Ryan A. Chi, Xuezhi Wang, Denny Zhou
2024Preventing Model Collapse in Gaussian Process Latent Variable Models.
Ying Li, Zhidi Lin, Feng Yin, Michael Minyi Zhang
2024Pricing with Contextual Elasticity and Heteroscedastic Valuation.
Jianyu Xu, Yu-Xiang Wang
2024Principled Gradient-Based MCMC for Conditional Sampling of Text.
Li Du, Afra Amini, Lucas Torroba Hennigen, Xinyan Velocity Yu, Holden Lee, Jason Eisner, Ryan Cotterell
2024Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF.
Han Shen, Zhuoran Yang, Tianyi Chen
2024Principled Preferential Bayesian Optimization.
Wenjie Xu, Wenbin Wang, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
2024Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis.
Shirin Shoushtari, Jiaming Liu, Edward P. Chandler, M. Salman Asif, Ulugbek S. Kamilov
2024PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses.
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
2024Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models.
Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, Dorsa Sadigh
2024Privacy Attacks in Decentralized Learning.
Abdellah El Mrini, Edwige Cyffers, Aurélien Bellet
2024Privacy Backdoors: Stealing Data with Corrupted Pretrained Models.
Shanglun Feng, Florian Tramèr
2024Privacy Preserving Adaptive Experiment Design.
Jiachun Li, Kaining Shi, David Simchi-Levi
2024Privacy Profiles for Private Selection.
Antti Koskela, Rachel Redberg, Yu-Xiang Wang
2024Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent.
Konstantin Donhauser, Javier Abad Martinez, Neha Hulkund, Fanny Yang
2024Privacy-Preserving Embedding via Look-up Table Evaluation with Fully Homomorphic Encryption.
Jaeyun Kim, Saerom Park, Joohee Lee, Jung Hee Cheon
2024Privacy-Preserving Instructions for Aligning Large Language Models.
Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu
2024Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation.
Gavin Brown, Krishnamurthy Dj Dvijotham, Georgina Evans, Daogao Liu, Adam Smith, Abhradeep Guha Thakurta
2024Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses.
Changyu Gao, Andrew Lowy, Xingyu Zhou, Stephen J. Wright
2024Private Truly-Everlasting Robust-Prediction.
Uri Stemmer
2024Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages.
Hilal Asi, Vitaly Feldman, Jelani Nelson, Huy L. Nguyen, Kunal Talwar, Samson Zhou
2024Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems.
Roie Reshef, Kfir Yehuda Levy
2024Privately Learning Smooth Distributions on the Hypercube by Projections.
Clément Lalanne, Sébastien Gadat
2024Proactive DP: A Multiple Target Optimization Framework for DP-SGD.
Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen, Phuong Ha Nguyen
2024Proactive Detection of Voice Cloning with Localized Watermarking.
Robin San Roman, Pierre Fernandez, Hady Elsahar, Alexandre Défossez, Teddy Furon, Tuan Tran
2024Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models.
Hengyi Wang, Shiwei Tan, Hao Wang
2024Probabilistic Constrained Reinforcement Learning with Formal Interpretability.
Yanran Wang, Qiuchen Qian, David Boyle
2024Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes.
Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas Matthew Boffi, Eric Vanden-Eijnden
2024Probabilistic Generating Circuits - Demystified.
Sanyam Agarwal, Markus Bläser
2024Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo.
Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Baker Grosse
2024Probabilistic Modeling of Interpersonal Coordination Processes.
Paulo Soares, Adarsh Pyarelal, Meghavarshini Krishnaswamy, Emily Butler, Kobus Barnard
2024Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search.
Kejing Lu, Chuan Xiao, Yoshiharu Ishikawa
2024Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning.
Vivienne Huiling Wang, Tinghuai Wang, Wenyan Yang, Joni-Kristian Kämäräinen, Joni Pajarinen
2024Probabilistic Time Series Modeling with Decomposable Denoising Diffusion Model.
Tijin Yan, Hengheng Gong, Yongping He, Yufeng Zhan, Yuanqing Xia
2024Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization.
Hao Wang, Kaifeng Yang, Michael Affenzeller
2024Prodigy: An Expeditiously Adaptive Parameter-Free Learner.
Konstantin Mishchenko, Aaron Defazio
2024Profile Reconstruction from Private Sketches.
Hao Wu, Rasmus Pagh
2024Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions.
Sanjay Kariyappa, Freddy Lécué, Saumitra Mishra, Christopher Pond, Daniele Magazzeni, Manuela Veloso
2024Projecting Molecules into Synthesizable Chemical Spaces.
Shitong Luo, Wenhao Gao, Zuofan Wu, Jian Peng, Connor W. Coley, Jianzhu Ma
2024Projection-Free Online Convex Optimization with Time-Varying Constraints.
Dan Garber, Ben Kretzu
2024Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization.
Wei Jiang, Sifan Yang, Wenhao Yang, Yibo Wang, Yuanyu Wan, Lijun Zhang
2024Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE.
Hao Wu, Huiyuan Wang, Kun Wang, Weiyan Wang, Changan Ye, Yangyu Tao, Chong Chen, Xian-Sheng Hua, Xiao Luo
2024Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines.
Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore Papineni, Sanjiv Kumar, Andrej Risteski
2024Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation.
Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu
2024Prompt Sketching for Large Language Models.
Luca Beurer-Kellner, Mark Niklas Müller, Marc Fischer, Martin T. Vechev
2024Prompt-based Visual Alignment for Zero-shot Policy Transfer.
Haihan Gao, Rui Zhang, Qi Yi, Hantao Yao, Haochen Li, Jiaming Guo, Shaohui Peng, Yunkai Gao, Qicheng Wang, Xing Hu, Yuanbo Wen, Zihao Zhang, Zidong Du, Ling Li, Qi Guo, Yunji Chen
2024Prompt-guided Precise Audio Editing with Diffusion Models.
Manjie Xu, Chenxing Li, Duzhen Zhang, Dan Su, Wei Liang, Dong Yu
2024Prompt-tuning Latent Diffusion Models for Inverse Problems.
Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio
2024Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution.
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel
2024Prompting a Pretrained Transformer Can Be a Universal Approximator.
Aleksandar Petrov, Philip Torr, Adel Bibi
2024Prompting is a Double-Edged Sword: Improving Worst-Group Robustness of Foundation Models.
Amrith Setlur, Saurabh Garg, Virginia Smith, Sergey Levine
2024Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts.
Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, Wei-Chen Chiu
2024Prospective Side Information for Latent MDPs.
Jeongyeol Kwon, Yonathan Efroni, Shie Mannor, Constantine Caramanis
2024Prospector Heads: Generalized Feature Attribution for Large Models & Data.
Gautam Machiraju, Alexander Derry, Arjun D. Desai, Neel Guha, Amir-Hossein Karimi, James Zou, Russ B. Altman, Christopher Ré, Parag Mallick
2024Protein Conformation Generation via Force-Guided SE(3) Diffusion Models.
Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu
2024Proteus: Exploring Protein Structure Generation for Enhanced Designability and Efficiency.
ChenTong Wang, Yannan Qu, Zhangzhi Peng, Yukai Wang, Hongli Zhu, Dachuan Chen, Longxing Cao
2024ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data.
Xiangjian Jiang, Andrei Margeloiu, Nikola Simidjievski, Mateja Jamnik
2024Prototypical Transformer As Unified Motion Learners.
Cheng Han, Yawen Lu, Guohao Sun, James Chenhao Liang, Zhiwen Cao, Qifan Wang, Qiang Guan, Sohail A. Dianat, Raghuveer Rao, Tong Geng, Zhiqiang Tao, Dongfang Liu
2024Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective.
Yajie Bao, Michael Crawshaw, Mingrui Liu
2024Provable Contrastive Continual Learning.
Yichen Wen, Zhiquan Tan, Kaipeng Zheng, Chuanlong Xie, Weiran Huang
2024Provable Interactive Learning with Hindsight Instruction Feedback.
Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
2024Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks.
Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai
2024Provable Privacy with Non-Private Pre-Processing.
Yaxi Hu, Amartya Sanyal, Bernhard Schölkopf
2024Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning.
Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, Bo Dai
2024Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation.
Yu Chen, Xiangcheng Zhang, Siwei Wang, Longbo Huang
2024Provably Better Explanations with Optimized Aggregation of Feature Attributions.
Thomas Decker, Ananta R. Bhattarai, Jindong Gu, Volker Tresp, Florian Buettner
2024Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret.
Han Zhong, Jiachen Hu, Yecheng Xue, Tongyang Li, Liwei Wang
2024Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization.
Liam Schramm, Abdeslam Boularias
2024Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation.
Tonghe Zhang, Yu Chen, Longbo Huang
2024Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback.
Guojun Xiong, Jian Li
2024Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples.
Dake Bu, Wei Huang, Taiji Suzuki, Ji Cheng, Qingfu Zhang, Zhiqiang Xu, Hau-San Wong
2024Provably Robust DPO: Aligning Language Models with Noisy Feedback.
Sayak Ray Chowdhury, Anush Kini, Nagarajan Natarajan
2024Provably Scalable Black-Box Variational Inference with Structured Variational Families.
Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner
2024PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency.
Yeonsung Jung, Heecheol Yun, Joonhyung Park, Jin-Hwa Kim, Eunho Yang
2024Pruned Pivot: Correlation Clustering Algorithm for Dynamic, Parallel, and Local Computation Models.
Mina Dalirrooyfard, Konstantin Makarychev, Slobodan Mitrovic
2024Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models.
Peijie Dong, Lujun Li, Zhenheng Tang, Xiang Liu, Xinglin Pan, Qiang Wang, Xiaowen Chu
2024Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation.
Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo
2024Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders.
Yi Yu, Yufei Wang, Song Xia, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex C. Kot
2024Purifying Quantization-conditioned Backdoors via Layer-wise Activation Correction with Distribution Approximation.
Boheng Li, Yishuo Cai, Jisong Cai, Yiming Li, Han Qiu, Run Wang, Tianwei Zhang
2024Pursuing Overall Welfare in Federated Learning through Sequential Decision Making.
Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee
2024Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels.
Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guangtao Zhai, Weisi Lin
2024Q-Probe: A Lightweight Approach to Reward Maximization for Language Models.
Kenneth Li, Samy Jelassi, Hugh Zhang, Sham M. Kakade, Martin Wattenberg, David Brandfonbrener
2024Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent.
Yingru Li, Jiawei Xu, Lei Han, Zhi-Quan Luo
2024Q-value Regularized Transformer for Offline Reinforcement Learning.
Shengchao Hu, Ziqing Fan, Chaoqin Huang, Li Shen, Ya Zhang, Yanfeng Wang, Dacheng Tao
2024QBMK: Quantum-based Matching Kernels for Un-attributed Graphs.
Lu Bai, Lixin Cui, Ming Li, Yue Wang, Edwin R. Hancock
2024QORA: Zero-Shot Transfer via Interpretable Object-Relational Model Learning.
Gabriel Stella, Dmitri Loguinov
2024QUEST: Query-Aware Sparsity for Efficient Long-Context LLM Inference.
Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, Song Han
2024QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.
Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa
2024QuRating: Selecting High-Quality Data for Training Language Models.
Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen
2024Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization.
Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, Joel Lehman
2024Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics.
Luca Grillotti, Maxence Faldor, Borja G. León, Antoine Cully
2024Quality-Diversity with Limited Resources.
Ren-Jian Wang, Ke Xue, Cong Guan, Chao Qian
2024Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design.
Quan Nguyen, Adji Bousso Dieng
2024Quantum Algorithm for Online Exp-concave Optimization.
Jianhao He, Chengchang Liu, Xutong Liu, Lvzhou Li, John C. S. Lui
2024Quantum Algorithms and Lower Bounds for Finite-Sum Optimization.
Yexin Zhang, Chenyi Zhang, Cong Fang, Liwei Wang, Tongyang Li
2024Quantum Implicit Neural Representations.
Jiaming Zhao, Wenbo Qiao, Peng Zhang, Hui Gao
2024Quantum Positional Encodings for Graph Neural Networks.
Slimane Thabet, Mehdi Djellabi, Igor Olegovich Sokolov, Sachin Kasture, Louis-Paul Henry, Loïc Henriet
2024Quantum Theory and Application of Contextual Optimal Transport.
Nicola Mariella, Albert Akhriev, Francesco Tacchino, Christa Zoufal, Juan Carlos Gonzalez-Espitia, Benedek Harsanyi, Eugene Koskin, Ivano Tavernelli, Stefan Woerner, Marianna Rapsomaniki, Sergiy Zhuk, Jannis Born
2024Quasi-Monte Carlo Features for Kernel Approximation.
Zhen Huang, Jiajin Sun, Yian Huang
2024R2E: Turning any Github Repository into a Programming Agent Environment.
Naman Jain, Manish Shetty, Tianjun Zhang, King Han, Koushik Sen, Ion Stoica
2024RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation.
Jiawei Zhou, Linye Lyu, Daojing He, Yu Li
2024REMEDI: Corrective Transformations for Improved Neural Entropy Estimation.
Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy, Pierre Nyquist
2024REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates.
Arshia Afzal, Grigorios Chrysos, Volkan Cevher, Mahsa Shoaran
2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation.
Zelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang, Gang Wang, Xinyu Xing
2024RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences.
Jie Cheng, Gang Xiong, Xingyuan Dai, Qinghai Miao, Yisheng Lv, Fei-Yue Wang
2024RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning.
Boning Li, Zhixuan Fang, Longbo Huang
2024RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback.
Yufei Wang, Zhanyi Sun, Jesse Zhang, Zhou Xian, Erdem Biyik, David Held, Zackory Erickson
2024RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback.
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, Sushant Prakash
2024RLVF: Learning from Verbal Feedback without Overgeneralization.
Moritz Stephan, Alexander Khazatsky, Eric Mitchell, Annie S. Chen, Sheryl Hsu, Archit Sharma, Chelsea Finn
2024RMIB: Representation Matching Information Bottleneck for Matching Text Representations.
Haihui Pan, Zhifang Liao, Wenrui Xie, Kun Han
2024RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching.
Divya Nori, Wengong Jin
2024RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples.
Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi, Masoud Hadi, Arshia Soltani Moakhar, Mohammad Azizmalayeri, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
2024RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning.
Yukinari Hisaki, Isao Ono
2024Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency.
Sudeep Salgia, Sattar Vakili, Qing Zhao
2024Random Latent Exploration for Deep Reinforcement Learning.
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
2024Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning.
Jing Xu, Jingzhao Zhang
2024Random Scaling and Momentum for Non-smooth Non-convex Optimization.
Qinzi Zhang, Ashok Cutkosky
2024Random features models: a way to study the success of naive imputation.
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet
2024Random matrix theory improved Fréchet mean of symmetric positive definite matrices.
Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frédéric Pascal
2024Randomized Confidence Bounds for Stochastic Partial Monitoring.
Maxime Heuillet, Ola Ahmad, Audrey Durand
2024Ranking-based Client Imitation Selection for Efficient Federated Learning.
Chunlin Tian, Zhan Shi, Xinpeng Qin, Li Li, Chengzhong Xu
2024Rapid Learning without Catastrophic Forgetting in the Morris Water Maze.
Raymond Wang, Jaedong Hwang, Akhilan Boopathy, Ila R. Fiete
2024Rate-Optimal Policy Optimization for Linear Markov Decision Processes.
Uri Sherman, Alon Cohen, Tomer Koren, Yishay Mansour
2024Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge.
Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li, Stan Z. Li
2024ReDiffuser: Reliable Decision-Making Using a Diffuser with Confidence Estimation.
Nantian He, Shaohui Li, Zhi Li, Yu Liu, You He
2024ReGAL: Refactoring Programs to Discover Generalizable Abstractions.
Elias Stengel-Eskin, Archiki Prasad, Mohit Bansal
2024ReLU Network with Width d+O(1) Can Achieve Optimal Approximation Rate.
Chenghao Liu, Minghua Chen
2024ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages.
Andrew Jesson, Chris Lu, Gunshi Gupta, Nicolas Beltran-Velez, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
2024ReLUs Are Sufficient for Learning Implicit Neural Representations.
Joseph Shenouda, Yamin Zhou, Robert D. Nowak
2024ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models.
Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, Zhi-Quan Luo
2024Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization.
Jian Liang, Lijun Sheng, Zhengbo Wang, Ran He, Tieniu Tan
2024Reason for Future, Act for Now: A Principled Architecture for Autonomous LLM Agents.
Zhihan Liu, Hao Hu, Shenao Zhang, Hongyi Guo, Shuqi Ke, Boyi Liu, Zhaoran Wang
2024Receptive Fields As Experts in Convolutional Neural Architectures.
Dongze Lian, Weihao Yu, Xinchao Wang
2024ReconBoost: Boosting Can Achieve Modality Reconcilement.
Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang, Qingming Huang
2024Recovering Labels from Local Updates in Federated Learning.
Huancheng Chen, Haris Vikalo
2024Recovering the Pre-Fine-Tuning Weights of Generative Models.
Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen
2024Recurrent Distance Filtering for Graph Representation Learning.
Yuhui Ding, Antonio Orvieto, Bobby He, Thomas Hofmann
2024Recurrent Early Exits for Federated Learning with Heterogeneous Clients.
Royson Lee, Javier Fernández-Marqués, Shell Xu Hu, Da Li, Stefanos Laskaridis, Lukasz Dudziak, Timothy M. Hospedales, Ferenc Huszár, Nicholas Donald Lane
2024Reducing Balancing Error for Causal Inference via Optimal Transport.
Yuguang Yan, Hao Zhou, Zeqin Yang, Weilin Chen, Ruichu Cai, Zhifeng Hao
2024Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation.
Yuchen Yang, Yingdong Shi, Cheems Wang, Xiantong Zhen, Yuxuan Shi, Jun Xu
2024Reducing Item Discrepancy via Differentially Private Robust Embedding Alignment for Privacy-Preserving Cross Domain Recommendation.
Weiming Liu, Xiaolin Zheng, Chaochao Chen, Jiahe Xu, Xinting Liao, Fan Wang, Yanchao Tan, Yew-Soon Ong
2024Reducing sequential change detection to sequential estimation.
Shubhanshu Shekhar, Aaditya Ramdas
2024Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion.
Xuantong Liu, Tianyang Hu, Wenjia Wang, Kenji Kawaguchi, Yuan Yao
2024Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations.
Ze Cheng, Zhongkai Hao, Xiaoqiang Wang, Jianing Huang, Youjia Wu, Xudan Liu, Yiru Zhao, Songming Liu, Hang Su
2024Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints.
Xiaobo Xia, Jiale Liu, Shaokun Zhang, Qingyun Wu, Hongxin Wei, Tongliang Liu
2024Refining Minimax Regret for Unsupervised Environment Design.
Michael Beukman, Samuel Coward, Michael T. Matthews, Mattie Fellows, Minqi Jiang, Michael D. Dennis, Jakob Nicolaus Foerster
2024Reflected Flow Matching.
Tianyu Xie, Yu Zhu, Longlin Yu, Tong Yang, Ziheng Cheng, Shiyue Zhang, Xiangyu Zhang, Cheng Zhang
2024Reflective Policy Optimization.
Yaozhong Gan, Renye Yan, Zhe Wu, Junliang Xing
2024Regression Learning with Limited Observations of Multivariate Outcomes and Features.
Yifan Sun, Grace Yi
2024Regression with Multi-Expert Deferral.
Anqi Mao, Mehryar Mohri, Yutao Zhong
2024Regularized Q-learning through Robust Averaging.
Peter Schmitt-Förster, Tobias Sutter
2024Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning.
Sungmin Cha, Kyunghyun Cho, Taesup Moon
2024Reinforcement Learning and Regret Bounds for Admission Control.
Lucas Weber, Ana Busic, Jiamin Zhu
2024Reinforcement Learning from Reachability Specifications: PAC Guarantees with Expected Conditional Distance.
Jakub Svoboda, Suguman Bansal, Krishnendu Chatterjee
2024Reinforcement Learning within Tree Search for Fast Macro Placement.
Zijie Geng, Jie Wang, Ziyan Liu, Siyuan Xu, Zhentao Tang, Mingxuan Yuan, Jianye Hao, Yongdong Zhang, Feng Wu
2024Reinformer: Max-Return Sequence Modeling for Offline RL.
Zifeng Zhuang, Dengyun Peng, Jinxin Liu, Ziqi Zhang, Donglin Wang
2024Rejuvenating image-GPT as Strong Visual Representation Learners.
Sucheng Ren, Zeyu Wang, Hongru Zhu, Junfei Xiao, Alan L. Yuille, Cihang Xie
2024Relational DNN Verification With Cross Executional Bound Refinement.
Debangshu Banerjee, Gagandeep Singh
2024Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective.
Yang Chen, Cong Fang, Zhouchen Lin, Bing Liu
2024Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise.
Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar
2024Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering.
Haoxuan Li, Chunyuan Zheng, Shuyi Wang, Kunhan Wu, Eric Hao Wang, Peng Wu, Zhi Geng, Xu Chen, Xiao-Hua Zhou
2024Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making.
Parand A. Alamdari, Toryn Q. Klassen, Elliot Creager, Sheila A. McIlraith
2024Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation.
Floris Holstege, Bram Wouters, Noud P. A. van Giersbergen, Cees G. H. Diks
2024Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks.
Yunfei Long, Zilin Tian, Liguo Zhang, Huosheng Xu
2024Repeat After Me: Transformers are Better than State Space Models at Copying.
Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach
2024Replicable Learning of Large-Margin Halfspaces.
Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas, Felix Zhou
2024Repoformer: Selective Retrieval for Repository-Level Code Completion.
Di Wu, Wasi Uddin Ahmad, Dejiao Zhang, Murali Krishna Ramanathan, Xiaofei Ma
2024Representation Surgery for Multi-Task Model Merging.
Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo, Xiaojun Chen, Xingwei Wang, Dacheng Tao
2024Representation Surgery: Theory and Practice of Affine Steering.
Shashwat Singh, Shauli Ravfogel, Jonathan Herzig, Roee Aharoni, Ryan Cotterell, Ponnurangam Kumaraguru
2024Representing Molecules as Random Walks Over Interpretable Grammars.
Michael Sun, Minghao Guo, Weize Yuan, Veronika Thost, Crystal Elaine Owens, Aristotle Franklin Grosz, Sharvaa Selvan, Katelyn Zhou, Hassan Mohiuddin, Benjamin J. Pedretti, Zachary P. Smith, Jie Chen, Wojciech Matusik
2024Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling.
Weijia Xu, Andrzej Banburski, Nebojsa Jojic
2024Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast.
Thomas Ferté, Dan Dutartre, Boris P. Hejblum, Romain Griffier, Vianney Jouhet, Rodolphe Thiébaut, Pierrick Legrand, Xavier Hinaut
2024Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems.
Bonan Zhang, Chia-Yu Chen, Naveen Verma
2024Residual Quantization with Implicit Neural Codebooks.
Iris A. M. Huijben, Matthijs Douze, Matthew J. Muckley, Ruud van Sloun, Jakob Verbeek
2024Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration.
Xiaole Tang, Xin Hu, Xiang Gu, Jian Sun
2024Resisting Stochastic Risks in Diffusion Planners with the Trajectory Aggregation Tree.
Lang Feng, Pengjie Gu, Bo An, Gang Pan
2024Restoring balance: principled under/oversampling of data for optimal classification.
Emanuele Loffredo, Mauro Pastore, Simona Cocco, Rémi Monasson
2024Rethinking Adversarial Robustness in the Context of the Right to be Forgotten.
Chenxu Zhao, Wei Qian, Yangyi Li, Aobo Chen, Mengdi Huai
2024Rethinking DP-SGD in Discrete Domain: Exploring Logistic Distribution in the Realm of signSGD.
Jonggyu Jang, Seongjin Hwang, Hyun Jong Yang
2024Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits.
Jiachen T. Wang, Tianji Yang, James Zou, Yongchan Kwon, Ruoxi Jia
2024Rethinking Decision Transformer via Hierarchical Reinforcement Learning.
Yi Ma, Jianye Hao, Hebin Liang, Chenjun Xiao
2024Rethinking Generative Large Language Model Evaluation for Semantic Comprehension.
Fangyun Wei, Xi Chen, Lin Luo
2024Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective.
Yulong Zhang, Yuan Yao, Shuhao Chen, Pengrong Jin, Yu Zhang, Jian Jin, Jiangang Lu
2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Data.
Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang, Xin Wang
2024Rethinking Momentum Knowledge Distillation in Online Continual Learning.
Nicolas Michel, Maorong Wang, Ling Xiao, Toshihiko Yamasaki
2024Rethinking Optimization and Architecture for Tiny Language Models.
Yehui Tang, Kai Han, Fangcheng Liu, Yunsheng Ni, Yuchuan Tian, Zheyuan Bai, Yi-Qi Hu, Sichao Liu, Shangling Jui, Yunhe Wang
2024Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion.
Bowen Gao, Minsi Ren, Yuyan Ni, Yanwen Huang, Bo Qiang, Zhi-Ming Ma, Wei-Ying Ma, Yanyan Lan
2024Rethinking Transformers in Solving POMDPs.
Chenhao Lu, Ruizhe Shi, Yuyao Liu, Kaizhe Hu, Simon Shaolei Du, Huazhe Xu
2024Rethinking the Flat Minima Searching in Federated Learning.
Taehwan Lee, Sung Whan Yoon
2024Retrieval Across Any Domains via Large-scale Pre-trained Model.
Jiexi Yan, Zhihui Yin, Chenghao Xu, Cheng Deng, Heng Huang
2024Retrieval-Augmented Score Distillation for Text-to-3D Generation.
Junyoung Seo, Susung Hong, Wooseok Jang, Inès Hyeonsu Kim, Minseop Kwak, Doyup Lee, Seungryong Kim
2024Revealing Vision-Language Integration in the Brain with Multimodal Networks.
Vighnesh Subramaniam, Colin Conwell, Christopher Wang, Gabriel Kreiman, Boris Katz, Ignacio Cases, Andrei Barbu
2024Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness.
Honghao Chen, Yurong Zhang, Xiaokun Feng, Xiangxiang Chu, Kaiqi Huang
2024Revisit the Essence of Distilling Knowledge through Calibration.
Wen-Shu Fan, Su Lu, Xin-Chun Li, De-Chuan Zhan, Le Gan
2024Revisiting Character-level Adversarial Attacks for Language Models.
Elías Abad-Rocamora, Yongtao Wu, Fanghui Liu, Grigorios Chrysos, Volkan Cevher
2024Revisiting Context Aggregation for Image Matting.
Qinglin Liu, Xiaoqian Lv, Quanling Meng, Zonglin Li, Xiangyuan Lan, Shuo Yang, Shengping Zhang, Liqiang Nie
2024Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity.
Ahmet Alacaoglu, Donghwan Kim, Stephen J. Wright
2024Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning.
Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood
2024Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark.
Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen
2024Revisiting the Power of Prompt for Visual Tuning.
Yuzhu Wang, Lechao Cheng, Chaowei Fang, Dingwen Zhang, Manni Duan, Meng Wang
2024Revisiting the Role of Language Priors in Vision-Language Models.
Zhiqiu Lin, Xinyue Chen, Deepak Pathak, Pengchuan Zhang, Deva Ramanan
2024Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling.
Guoqi Yu, Jing Zou, Xiaowei Hu, Angelica I. Avilés-Rivero, Jing Qin, Shujun Wang
2024Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences.
Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban, Georgios Tzannetos, Goran Radanovic, Adish Singla
2024Reward Shaping for Reinforcement Learning with An Assistant Reward Agent.
Haozhe Ma, Kuankuan Sima, Thanh Vinh Vo, Di Fu, Tze-Yun Leong
2024Reward-Free Kernel-Based Reinforcement Learning.
Sattar Vakili, Farhang Nabiei, Da-Shan Shiu, Alberto Bernacchia
2024Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment.
Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu, Han Zhong, Dong Yu, Jianshu Chen
2024Reweighted Solutions for Weighted Low Rank Approximation.
David P. Woodruff, Taisuke Yasuda
2024Rich-Observation Reinforcement Learning with Continuous Latent Dynamics.
Yuda Song, Lili Wu, Dylan J. Foster, Akshay Krishnamurthy
2024Riemannian Accelerated Zeroth-order Algorithm: Improved Robustness and Lower Query Complexity.
Chang He, Zhaoye Pan, Xiao Wang, Bo Jiang
2024Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models.
Fangzhao Zhang, Mert Pilanci
2024Riemannian coordinate descent algorithms on matrix manifolds.
Andi Han, Pratik Jawanpuria, Bamdev Mishra
2024RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content.
Zhuowen Yuan, Zidi Xiong, Yi Zeng, Ning Yu, Ruoxi Jia, Dawn Song, Bo Li
2024Risk Aware Benchmarking of Large Language Models.
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti, Kristjan H. Greenewald, Brian Belgodere, Mikhail Yurochkin, Jirí Navrátil, Igor Melnyk, Jarret Ross
2024Risk Estimation in a Markov Cost Process: Lower and Upper Bounds.
Gugan Thoppe, Prashanth L. A., Sanjay P. Bhat
2024Risk-Sensitive Policy Optimization via Predictive CVaR Policy Gradient.
Ju-Hyun Kim, Seungki Min
2024Risk-Sensitive Reward-Free Reinforcement Learning with CVaR.
Xinyi Ni, Guanlin Liu, Lifeng Lai
2024RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.
Mahdi Nikdan, Soroush Tabesh, Elvir Crncevic, Dan Alistarh
2024RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis.
Yao Mu, Junting Chen, Qinglong Zhang, Shoufa Chen, Qiaojun Yu, Chongjian Ge, Runjian Chen, Zhixuan Liang, Mengkang Hu, Chaofan Tao, Peize Sun, Haibao Yu, Chao Yang, Wenqi Shao, Wenhai Wang, Jifeng Dai, Yu Qiao, Mingyu Ding, Ping Luo
2024RoboDreamer: Learning Compositional World Models for Robot Imagination.
Siyuan Zhou, Yilun Du, Jiaben Chen, Yandong Li, Dit-Yan Yeung, Chuang Gan
2024RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation.
Yufei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Katerina Fragkiadaki, Zackory Erickson, David Held, Chuang Gan
2024RoboMP2: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models.
Qi Lv, Hao Li, Xiang Deng, Rui Shao, Michael Yu Wang, Liqiang Nie
2024Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models.
Christian Schlarmann, Naman Deep Singh, Francesco Croce, Matthias Hein
2024Robust Classification via a Single Diffusion Model.
Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, Jun Zhu
2024Robust Data-driven Prescriptiveness Optimization.
Mehran Poursoltani, Erick Delage, Angelos Georghiou
2024Robust Graph Matching when Nodes are Corrupt.
Taha Ameen, Bruce E. Hajek
2024Robust Inverse Constrained Reinforcement Learning under Model Misspecification.
Sheng Xu, Guiliang Liu
2024Robust Inverse Graphics via Probabilistic Inference.
Tuan Anh Le, Pavel Sountsov, Matthew Douglas Hoffman, Ben Lee, Brian Patton, Rif A. Saurous
2024Robust Learning-Augmented Dictionaries.
Ali Zeynali, Shahin Kamali, Mohammad Hajiesmaili
2024Robust Multi-Task Learning with Excess Risks.
Yifei He, Shiji Zhou, Guojun Zhang, Hyokun Yun, Yi Xu, Belinda Zeng, Trishul Chilimbi, Han Zhao
2024Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space.
Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung, Hyun Joo Ro, Meeyoung Cha, Ho Min Kim
2024Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination.
Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas
2024Robust Stable Spiking Neural Networks.
Jianhao Ding, Zhiyu Pan, Yujia Liu, Zhaofei Yu, Tiejun Huang
2024Robust Universal Adversarial Perturbations.
Changming Xu, Gagandeep Singh
2024Robust Yet Efficient Conformal Prediction Sets.
Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski
2024Robust and Conjugate Gaussian Process Regression.
Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch
2024Robustly Learning Single-Index Models via Alignment Sharpness.
Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas
2024Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data.
Kang Lin, Reinhard Heckel
2024Robustness of Nonlinear Representation Learning.
Simon Buchholz, Bernhard Schölkopf
2024Rolling Diffusion Models.
David Ruhe, Jonathan Heek, Tim Salimans, Emiel Hoogeboom
2024Roping in Uncertainty: Robustness and Regularization in Markov Games.
Jeremy McMahan, Giovanni Artiglio, Qiaomin Xie
2024Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks.
Atli Kosson, Bettina Messmer, Martin Jaggi
2024Run-Time Task Composition with Safety Semantics.
Kevin Leahy, Makai Mann, Zachary Serlin
2024Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas.
Clément Pierquin, Aurélien Bellet, Marc Tommasi, Matthieu Boussard
2024S2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting.
Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
2024S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning.
Guancheng Wan, Yijun Tian, Wenke Huang, Nitesh V. Chawla, Mang Ye
2024S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video.
Hao Zhang, Fang Li, Samyak Rawlekar, Narendra Ahuja
2024SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation.
Danni Yang, Jiayi Ji, Yiwei Ma, Tianyu Guo, Haowei Wang, Xiaoshuai Sun, Rongrong Ji
2024SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation.
Junjie Zhang, Chenjia Bai, Haoran He, Zhigang Wang, Bin Zhao, Xiu Li, Xuelong Li
2024SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention.
Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux, Giuseppe Paolo, Themis Palpanas, Ievgen Redko
2024SAPG: Split and Aggregate Policy Gradients.
Jayesh Singla, Ananye Agarwal, Deepak Pathak
2024SCoRe: Submodular Combinatorial Representation Learning.
Anay Majee, Suraj Kothawade, Krishnateja Killamsetty, Rishabh K. Iyer
2024SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning.
Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang
2024SFC: Achieve Accurate Fast Convolution under Low-precision Arithmetic.
Liulu He, Yufei Zhao, Rui Gao, Yuan Du, Li Du
2024SHINE: Shielding Backdoors in Deep Reinforcement Learning.
Zhuowen Yuan, Wenbo Guo, Jinyuan Jia, Bo Li, Dawn Song
2024SILVER: Single-loop variance reduction and application to federated learning.
Kazusato Oko, Shunta Akiyama, Denny Wu, Tomoya Murata, Taiji Suzuki
2024SIN: Selective and Interpretable Normalization for Long-Term Time Series Forecasting.
Lu Han, Han-Jia Ye, De-Chuan Zhan
2024SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization.
Jialong Guo, Xinghao Chen, Yehui Tang, Yunhe Wang
2024SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks.
Jiwon Song, Kyungseok Oh, Taesu Kim, Hyungjun Kim, Yulhwa Kim, Jae-Joon Kim
2024SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter.
Haobo Xu, Yuchen Yan, Dingsu Wang, Zhe Xu, Zhichen Zeng, Tarek F. Abdelzaher, Jiawei Han, Hanghang Tong
2024SMaRt: Improving GANs with Score Matching Regularity.
Mengfei Xia, Yujun Shen, Ceyuan Yang, Ran Yi, Wenping Wang, Yongjin Liu
2024SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity.
Tianshu Chu, Dachuan Xu, Wei Yao, Jin Zhang
2024SPADE: Sparsity-Guided Debugging for Deep Neural Networks.
Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar, Dan Alistarh
2024SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models.
Dongyang Liu, Renrui Zhang, Longtian Qiu, Siyuan Huang, Weifeng Lin, Shitian Zhao, Shijie Geng, Ziyi Lin, Peng Jin, Kaipeng Zhang, Wenqi Shao, Chao Xu, Conghui He, Junjun He, Hao Shao, Pan Lu, Yu Qiao, Hongsheng Li, Peng Gao
2024SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models.
Xudong Lu, Aojun Zhou, Yuhui Xu, Renrui Zhang, Peng Gao, Hongsheng Li
2024SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification.
Yehui Tang, Nianzu Yang, Mabiao Long, Junchi Yan
2024STEER: Assessing the Economic Rationality of Large Language Models.
Narun Krishnamurthi Raman, Taylor Lundy, Samuel Joseph Amouyal, Yoav Levine, Kevin Leyton-Brown, Moshe Tennenholtz
2024STELLA: Continual Audio-Video Pre-training with SpatioTemporal Localized Alignment.
Jaewoo Lee, Jaehong Yoon, Wonjae Kim, Yunji Kim, Sung Ju Hwang
2024SaVeR: Optimal Data Collection Strategy for Safe Policy Evaluation in Tabular MDP.
Subhojyoti Mukherjee, Josiah P. Hanna, Robert D. Nowak
2024Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants.
Isabel Chien, Wessel P. Bruinsma, Javier González Hernández, Richard E. Turner
2024Safe Reinforcement Learning using Finite-Horizon Gradient-based Estimation.
Juntao Dai, Yaodong Yang, Qian Zheng, Gang Pan
2024Safe and Robust Subgame Exploitation in Imperfect Information Games.
Zhenxing Ge, Zheng Xu, Tianyu Ding, Linjian Meng, Bo An, Wenbin Li, Yang Gao
2024Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models.
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, Timothy M. Hospedales
2024Saliency strikes back: How filtering out high frequencies improves white-box explanations.
Sabine Muzellec, Thomas Fel, Victor Boutin, Léo Andéol, Rufin VanRullen, Thomas Serre
2024Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data.
Yafei Wang, Bo Pan, Mei Li, Jianya Lu, Lingchen Kong, Bei Jiang, Linglong Kong
2024Sample Complexity Bounds for Estimating Probability Divergences under Invariances.
Behrooz Tahmasebi, Stefanie Jegelka
2024Sample as you Infer: Predictive Coding with Langevin Dynamics.
Umais Zahid, Qinghai Guo, Zafeirios Fountas
2024Sample-Efficient Multiagent Reinforcement Learning with Reset Replay.
Yaodong Yang, Guangyong Chen, Jianye Hao, Pheng-Ann Heng
2024Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty.
Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman
2024Sample-specific Masks for Visual Reprogramming-based Prompting.
Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, Feng Liu
2024Sampling in Unit Time with Kernel Fisher-Rao Flow.
Aimee Maurais, Youssef M. Marzouk
2024Sampling is as easy as keeping the consistency: convergence guarantee for Consistency Models.
Junlong Lyu, Zhitang Chen, Shoubo Feng
2024Sampling-based Multi-dimensional Recalibration.
Youngseog Chung, Ian Char, Jeff Schneider
2024Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features.
Aleksandr Beznosikov, David Dobre, Gauthier Gidel
2024Scalable AI Safety via Doubly-Efficient Debate.
Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras
2024Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers.
Katherine Crowson, Stefan Andreas Baumann, Alex Birch, Tanishq Mathew Abraham, Daniel Z. Kaplan, Enrico Shippole
2024Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation.
Weixuan Liang, En Zhu, Shengju Yu, Huiying Xu, Xinzhong Zhu, Xinwang Liu
2024Scalable Online Exploration via Coverability.
Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy
2024Scalable Pre-training of Large Autoregressive Image Models.
Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai, Miguel Ángel Bautista, Vaishaal Shankar, Alexander T. Toshev, Joshua M. Susskind, Armand Joulin
2024Scalable Safe Policy Improvement for Factored Multi-Agent MDPs.
Federico Bianchi, Edoardo Zorzi, Alberto Castellini, Thiago D. Simão, Matthijs T. J. Spaan, Alessandro Farinelli
2024Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport.
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency.
Alan Nawzad Amin, Andrew Gordon Wilson
2024Scale-Free Image Keypoints Using Differentiable Persistent Homology.
Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno, Marco Guerra, Gabriele Moreno Berton, Carlo Masone
2024Scaling Beyond the GPU Memory Limit for Large Mixture-of-Experts Model Training.
Yechan Kim, Hwijoon Lim, Dongsu Han
2024Scaling Down Deep Learning with MNIST-1D.
Samuel Greydanus, Dmitry Kobak
2024Scaling Exponents Across Parameterizations and Optimizers.
Katie E. Everett, Lechao Xiao, Mitchell Wortsman, Alexander A. Alemi, Roman Novak, Peter J. Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, Jeffrey Pennington
2024Scaling Laws for Fine-Grained Mixture of Experts.
Jan Ludziejewski, Jakub Krajewski, Kamil Adamczewski, Maciej Pióro, Michal Krutul, Szymon Antoniak, Kamil Ciebiera, Krystian Król, Tomasz Odrzygózdz, Piotr Sankowski, Marek Cygan, Sebastian Jaszczur
2024Scaling Laws for the Value of Individual Data Points in Machine Learning.
Ian Connick Covert, Wenlong Ji, Tatsunori Hashimoto, James Zou
2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Robin Rombach
2024Scaling Tractable Probabilistic Circuits: A Systems Perspective.
Anji Liu, Kareem Ahmed, Guy Van den Broeck
2024Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency.
Hyeongjin Kim, Sangwon Kim, Dasom Ahn, Jong Taek Lee, Byoung Chul Ko
2024SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender Code.
Ziniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf, Yisong Yue, David A. Ross, Cordelia Schmid, Alireza Fathi
2024SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models.
Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R. Loomba, Shichang Zhang, Yizhou Sun, Wei Wang
2024Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation.
Mingyuan Zhou, Huangjie Zheng, Zhendong Wang, Mingzhang Yin, Hai Huang
2024Score-Based Causal Discovery of Latent Variable Causal Models.
Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang
2024Scribble-Supervised Semantic Segmentation with Prototype-based Feature Augmentation.
Guiyang Chan, Pengcheng Zhang, Hai Dong, Shunhui Ji, Bainian Chen
2024SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets.
Shenghua Wan, Ziyuan Chen, Le Gan, Shuai Feng, De-Chuan Zhan
2024Second-Order Uncertainty Quantification: A Distance-Based Approach.
Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
2024See More Details: Efficient Image Super-Resolution by Experts Mining.
Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yulun Zhang, Radu Timofte
2024Seesaw: Compensating for Nonlinear Reduction with Linear Computations for Private Inference.
Fabing Li, Yuanhao Zhai, Shuangyu Cai, Mingyu Gao
2024Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic.
Tianying Ji, Yu Luo, Fuchun Sun, Xianyuan Zhan, Jianwei Zhang, Huazhe Xu
2024SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching.
Yongmin Lee, Hye Won Chung
2024Selecting Large Language Model to Fine-tune via Rectified Scaling Law.
Haowei Lin, Baizhou Huang, Haotian Ye, Qinyu Chen, Zihao Wang, Sujian Li, Jianzhu Ma, Xiaojun Wan, James Zou, Yitao Liang
2024Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup.
Damien Teney, Jindong Wang, Ehsan Abbasnejad
2024Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation.
Xianghe Pang, Shuo Tang, Rui Ye, Yuxin Xiong, Bolun Zhang, Yanfeng Wang, Siheng Chen
2024Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes.
Yingyi Chen, Qinghua Tao, Francesco Tonin, Johan A. K. Suykens
2024Self-Composing Policies for Scalable Continual Reinforcement Learning.
Mikel Malagón, Josu Ceberio, José Antonio Lozano
2024Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction.
He Zhang, Chang Liu, Zun Wang, Xinran Wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu
2024Self-Correcting Self-Consuming Loops for Generative Model Training.
Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun
2024Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning.
Wenke Huang, Zekun Shi, Mang Ye, He Li, Bo Du
2024Self-Infilling Code Generation.
Lin Zheng, Jianbo Yuan, Zhi Zhang, Hongxia Yang, Lingpeng Kong
2024Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.
Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu
2024Self-Rewarding Language Models.
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, Jason Weston
2024Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation.
Sergei Shumilin, Alexander Ryabov, Nikolay B. Yavich, Evgeny Burnaev, Vladimir Vanovskiy
2024Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity.
Hyunki Seong, David Hyunchul Shim
2024Self-attention Networks Localize When QK-eigenspectrum Concentrates.
Han Bao, Ryuichiro Hataya, Ryo Karakida
2024Self-cognitive Denoising in the Presence of Multiple Noisy Label Sources.
Yi-Xuan Sun, Ya-Lin Zhang, Bin Han, Longfei Li, Jun Zhou
2024SelfIE: Self-Interpretation of Large Language Model Embeddings.
Haozhe Chen, Carl Vondrick, Chengzhi Mao
2024SelfVC: Voice Conversion With Iterative Refinement using Self Transformations.
Paarth Neekhara, Shehzeen Samarah Hussain, Rafael Valle, Boris Ginsburg, Rishabh Ranjan, Shlomo Dubnov, Farinaz Koushanfar, Julian J. McAuley
2024Semantic-Aware Human Object Interaction Image Generation.
Zhu Xu, Qingchao Chen, Yuxin Peng, Yang Liu
2024Semantically-correlated memories in a dense associative model.
Thomas F. Burns
2024Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning.
Aditya A. Ramesh, Kenny John Young, Louis Kirsch, Jürgen Schmidhuber
2024Sequential Asynchronous Action Coordination in Multi-Agent Systems: A Stackelberg Decision Transformer Approach.
Bin Zhang, Hangyu Mao, Lijuan Li, Zhiwei Xu, Dapeng Li, Rui Zhao, Guoliang Fan
2024Sequential Disentanglement by Extracting Static Information From A Single Sequence Element.
Nimrod Berman, Ilan Naiman, Idan Arbiv, Gal Fadlon, Omri Azencot
2024Sequential Kernel Goodness-of-fit Testing.
Zhengyu Zhou, Weiwei Liu
2024Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models.
Louis Sharrock, Jack Simons, Song Liu, Mark Beaumont
2024Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss.
Ingvar M. Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni
2024Sharpness-Aware Data Generation for Zero-shot Quantization.
Hoang Anh Dung, Cuong Pham, Trung Le, Jianfei Cai, Thanh-Toan Do
2024Shifted Interpolation for Differential Privacy.
Jinho Bok, Weijie J. Su, Jason M. Altschuler
2024Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences.
Zicheng Liu, Siyuan Li, Li Wang, Zedong Wang, Yunfan Liu, Stan Z. Li
2024Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs.
Andries P. Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D. Barrett, Arnu Pretorius
2024SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States.
Noga Mudrik, Gal Mishne, Adam S. Charles
2024SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning.
Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
2024Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network.
Hyunseok Oh, Youngki Lee
2024Sign Rank Limitations for Inner Product Graph Decoders.
Su Hyeong Lee, Qingqi Zhang, Risi Kondor
2024Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs.
Langzhang Liang, Sunwoo Kim, Kijung Shin, Zenglin Xu, Shirui Pan, Yuan Qi
2024SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding.
Chanho Park, Namyoon Lee
2024SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning.
Chaoqun Du, Yizeng Han, Gao Huang
2024Simple Ingredients for Offline Reinforcement Learning.
Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati
2024Simple linear attention language models balance the recall-throughput tradeoff.
Simran Arora, Sabri Eyuboglu, Michael Zhang, Aman Timalsina, Silas Alberti, James Zou, Atri Rudra, Christopher Ré
2024Simplicity Bias of Two-Layer Networks beyond Linearly Separable Data.
Nikita Tsoy, Nikola Konstantinov
2024Simplicity Bias via Global Convergence of Sharpness Minimization.
Khashayar Gatmiry, Zhiyuan Li, Sashank J. Reddi, Stefanie Jegelka
2024Simulation of Graph Algorithms with Looped Transformers.
Artur Back de Luca, Kimon Fountoulakis
2024Simulation-Based Inference with Quantile Regression.
He Jia
2024Simultaneous identification of models and parameters of scientific simulators.
Cornelius Schröder, Jakob H. Macke
2024Single-Model Attribution of Generative Models Through Final-Layer Inversion.
Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer
2024Single-Trajectory Distributionally Robust Reinforcement Learning.
Zhipeng Liang, Xiaoteng Ma, José H. Blanchet, Jun Yang, Jiheng Zhang, Zhengyuan Zhou
2024Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection.
Feiran Li, Qianqian Xu, Shilong Bao, Zhiyong Yang, Runmin Cong, Xiaochun Cao, Qingming Huang
2024Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills.
Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Sameer Singh, Peter Clark, Roy Fox
2024SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals.
Rahul Thapa, Bryan He, Magnus Ruud Kjær, Hyatt E. Moore IV, Gauri Ganjoo, Emmanuel Mignot, James Zou
2024Sliced Wasserstein with Random-Path Projecting Directions.
Khai Nguyen, Shujian Zhang, Tam Le, Nhat Ho
2024Sliced-Wasserstein Estimation with Spherical Harmonics as Control Variates.
Rémi Leluc, Aymeric Dieuleveut, François Portier, Johan Segers, Aigerim Zhuman
2024Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices.
Nathaniel Cohen, Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli
2024Slicing Mutual Information Generalization Bounds for Neural Networks.
Kimia Nadjahi, Kristjan H. Greenewald, Rickard Brüel Gabrielsson, Justin Solomon
2024Sliding Down the Stairs: How Correlated Latent Variables Accelerate Learning with Neural Networks.
Lorenzo Bardone, Sebastian Goldt
2024Slot Abstractors: Toward Scalable Abstract Visual Reasoning.
Shanka Subhra Mondal, Jonathan D. Cohen, Taylor Whittington Webb
2024Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks.
Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle
2024Small-loss Adaptive Regret for Online Convex Optimization.
Wenhao Yang, Wei Jiang, Yibo Wang, Ping Yang, Yao Hu, Lijun Zhang
2024Smooth Min-Max Monotonic Networks.
Christian Igel
2024Smooth Tchebycheff Scalarization for Multi-Objective Optimization.
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Fei Liu, Zhenkun Wang, Qingfu Zhang
2024Smoothing Proximal Gradient Methods for Nonsmooth Sparsity Constrained Optimization: Optimality Conditions and Global Convergence.
Ganzhao Yuan
2024Smoothness Adaptive Hypothesis Transfer Learning.
Haotian Lin, Matthew Reimherr
2024Sobolev Space Regularised Pre Density Models.
Mark Kozdoba, Binyamin Perets, Shie Mannor
2024Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration.
Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Jialu Li, Weihao Li, Qinghua Hu
2024Soft Prompt Recovers Compressed LLMs, Transferably.
Zhaozhuo Xu, Zirui Liu, Beidi Chen, Shaochen (Henry) Zhong, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu, Anshumali Shrivastava
2024Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach.
Johan Peralez, Aurélien Delage, Olivier Buffet, Jilles Steeve Dibangoye
2024Solving Poisson Equations using Neural Walk-on-Spheres.
Hong Chul Nam, Julius Berner, Anima Anandkumar
2024SparQ Attention: Bandwidth-Efficient LLM Inference.
Luka Ribar, Ivan Chelombiev, Luke Hudlass-Galley, Charlie Blake, Carlo Luschi, Douglas Orr
2024Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once.
Zhangheng Li, Shiwei Liu, Tianlong Chen, Ajay Kumar Jaiswal, Zhenyu Zhang, Dilin Wang, Raghuraman Krishnamoorthi, Shiyu Chang, Zhangyang Wang
2024Sparse Dimensionality Reduction Revisited.
Mikael Møller Høgsgaard, Lior Kamma, Kasper Green Larsen, Jelani Nelson, Chris Schwiegelshohn
2024Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference.
Jian Xu, Delu Zeng, John W. Paisley
2024Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications.
Zixuan Hu, Yongxian Wei, Li Shen, Zhenyi Wang, Lei Li, Chun Yuan, Dacheng Tao
2024Sparse and Structured Hopfield Networks.
Saul José Rodrigues dos Santos, Vlad Niculae, Daniel C. McNamee, André F. T. Martins
2024Sparse is Enough in Fine-tuning Pre-trained Large Language Models.
Weixi Song, Zuchao Li, Lefei Zhang, Hai Zhao, Bo Du
2024Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency.
Vithursan Thangarasa, Shreyas Saxena, Abhay Gupta, Sean Lie
2024Sparse-to-dense Multimodal Image Registration via Multi-Task Learning.
Kaining Zhang, Jiayi Ma
2024SparseTSF: Modeling Long-term Time Series Forecasting with *1k* Parameters.
Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang
2024Sparser, Better, Deeper, Stronger: Improving Static Sparse Training with Exact Orthogonal Initialization.
Aleksandra Nowak, Lukasz Gniecki, Filip Szatkowski, Jacek Tabor
2024Sparsest Models Elude Pruning: An Exposé of Pruning's Current Capabilities.
Stephen Zhang, Vardan Papyan
2024Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models.
Pierre Mergny, Justin Ko, Florent Krzakala
2024Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions.
Nikita Doikov, Sebastian U. Stich, Martin Jaggi
2024Speech Self-Supervised Learning Using Diffusion Model Synthetic Data.
Heting Gao, Kaizhi Qian, Junrui Ni, Chuang Gan, Mark A. Hasegawa-Johnson, Shiyu Chang, Yang Zhang
2024Spider: A Unified Framework for Context-dependent Concept Segmentation.
Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, Huchuan Lu
2024Spike Distance Function as a Learning Objective for Spike Prediction.
Kevin Doran, Marvin Seifert, Carola A. M. Yovanovich, Tom Baden
2024SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms.
Xingrun Xing, Zheng Zhang, Ziyi Ni, Shitao Xiao, Yiming Ju, Siqi Fan, Yequan Wang, Jiajun Zhang, Guoqi Li
2024SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN.
Kang You, Zekai Xu, Chen Nie, Zhijie Deng, Qinghai Guo, Xiang Wang, Zhezhi He
2024Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting.
Anthony Chen, Huanrui Yang, Yulu Gan, Denis A. Gudovskiy, Zhen Dong, Haofan Wang, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, Shanghang Zhang
2024Split-and-Denoise: Protect large language model inference with local differential privacy.
Peihua Mai, Ran Yan, Zhe Huang, Youjia Yang, Yan Pang
2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text.
Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
2024SqueezeLLM: Dense-and-Sparse Quantization.
Sehoon Kim, Coleman Hooper, Amir Gholami, Zhen Dong, Xiuyu Li, Sheng Shen, Michael W. Mahoney, Kurt Keutzer
2024Stability Evaluation through Distributional Perturbation Analysis.
José H. Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu
2024Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to K-Level Stochastic Optimizations.
Xiaokang Pan, Xingyu Li, Jin Liu, Tao Sun, Kai Sun, Lixing Chen, Zhe Qu
2024Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms.
Ming Yang, Xiyuan Wei, Tianbao Yang, Yiming Ying
2024Stability and Multigroup Fairness in Ranking with Uncertain Predictions.
Siddartha Devic, Aleksandra Korolova, David Kempe, Vatsal Sharan
2024Stability-Informed Initialization of Neural Ordinary Differential Equations.
Theodor Westny, Arman Mohammadi, Daniel Jung, Erik Frisk
2024Stabilizing Policy Gradients for Stochastic Differential Equations via Consistency with Perturbation Process.
Xiangxin Zhou, Liang Wang, Yichi Zhou
2024Stable Differentiable Causal Discovery.
Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei
2024StableMask: Refining Causal Masking in Decoder-only Transformer.
Qingyu Yin, Xuzheng He, Xiang Zhuang, Yu Zhao, Jianhua Yao, Xiaoyu Shen, Qiang Zhang
2024StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization.
Shida Wang, Qianxiao Li
2024StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation.
Weike Fang, Zhejian Zhou, Junzhou He, Weihang Wang
2024Stacking Deep Set Networks and Pooling by Quantiles.
Zhuojun Chen, Xinghua Zhu, Dongzhe Su, Justin C. I. Chuang
2024Standardized Interpretable Fairness Measures for Continuous Risk Scores.
Ann-Kristin Becker, Oana Dumitrasc, Klaus Broelemann
2024State-Constrained Zero-Sum Differential Games with One-Sided Information.
Mukesh Ghimire, Lei Zhang, Zhe Xu, Yi Ren
2024State-Free Inference of State-Space Models: The *Transfer Function* Approach.
Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T. H. Smith, Ramin M. Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, Taiji Suzuki, Michael Poli, Atsushi Yamashita
2024Stationarity without mean reversion in improper Gaussian processes.
Luca Ambrogioni
2024Stationary Latent Weight Inference for Unreliable Observations from Online Test-Time Adaptation.
Jae-Hong Lee, Joon-Hyuk Chang
2024Statistical Inference Under Constrained Selection Bias.
Santiago Cortes-Gomez, Mateo Dulce Rubio, Carlos Miguel Patiño, Bryan Wilder
2024Statistical Properties of Robust Satisficing.
Zhiyi Li, Yunbei Xu, Ruohan Zhan
2024Statistical Test for Attention Maps in Vision Transformers.
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka, Vo Nguyen Le Duy, Kouichi Taji, Ichiro Takeuchi
2024Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution.
Elen Vardanyan, Sona Hunanyan, Tigran Galstyan, Arshak Minasyan, Arnak S. Dalalyan
2024Stay on Topic with Classifier-Free Guidance.
Guillaume Sanchez, Alexander Spangher, Honglu Fan, Elad Levi, Stella Biderman
2024Stealing part of a production language model.
Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr
2024Stealthy Imitation: Reward-guided Environment-free Policy Stealing.
Zhixiong Zhuang, Maria-Irina Nicolae, Mario Fritz
2024Stereo Risk: A Continuous Modeling Approach to Stereo Matching.
Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Yao Yao, Luc Van Gool
2024Stereographic Spherical Sliced Wasserstein Distances.
Huy Tran, Yikun Bai, Abihith Kothapalli, Ashkan Shahbazi, Xinran Liu, Rocio Diaz Martin, Soheil Kolouri
2024Stochastic Bandits with ReLU Neural Networks.
Kan Xu, Hamsa Bastani, Surbhi Goel, Osbert Bastani
2024Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis.
Juyeon Ko, Inho Kong, Dogyun Park, Hyunwoo J. Kim
2024Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features.
Rodrigo Veiga, Anastasia Remizova, Nicolas Macris
2024Stochastic Interpolants with Data-Dependent Couplings.
Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
2024Stochastic Localization via Iterative Posterior Sampling.
Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus
2024Stochastic Optimization with Arbitrary Recurrent Data Sampling.
William G. Powell, Hanbaek Lyu
2024Stochastic Q-learning for Large Discrete Action Spaces.
Fares Fourati, Vaneet Aggarwal, Mohamed-Slim Alouini
2024Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions.
Guneykan Ozgul, Xiantao Li, Mehrdad Mahdavi, Chunhao Wang
2024Stochastic Weakly Convex Optimization beyond Lipschitz Continuity.
Wenzhi Gao, Qi Deng
2024Stochastic positional embeddings improve masked image modeling.
Amir Bar, Florian Bordes, Assaf Shocher, Mido Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann LeCun
2024Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.
Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal
2024StrWAEs to Invariant Representations.
Hyunjong Lee, Yedarm Seong, Sungdong Lee, Joong-Ho Won
2024Straight-Through Meets Sparse Recovery: the Support Exploration Algorithm.
Mimoun Mohamed, François Malgouyres, Valentin Emiya, Caroline Chaux
2024StrokeNUWA - Tokenizing Strokes for Vector Graphic Synthesis.
Zecheng Tang, Chenfei Wu, Zekai Zhang, Minheng Ni, Shengming Yin, Yu Liu, Zhengyuan Yang, Lijuan Wang, Zicheng Liu, Juntao Li, Nan Duan
2024Structure Your Data: Towards Semantic Graph Counterfactuals.
Angeliki Dimitriou, Maria Lymperaiou, Giorgos Filandrianos, Konstantinos Thomas, Giorgos Stamou
2024Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks.
Duy Minh Ho Nguyen, Nina Lukashina, Tai Nguyen, An T. Le, TrungTin Nguyen, Nhat Ho, Jan Peters, Daniel Sonntag, Viktor Zaverkin, Mathias Niepert
2024Structure-based drug design by denoising voxel grids.
Pedro O. Pinheiro, Arian Rokkum Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi
2024Structured Chemistry Reasoning with Large Language Models.
Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Yejin Choi, Jiawei Han, Lianhui Qin
2024Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC.
Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
2024Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens.
Ross M. Clarke, José Miguel Hernández-Lobato
2024StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization.
Songhua Liu, Xin Jin, Xingyi Yang, Jingwen Ye, Xinchao Wang
2024SuDA: Support-based Domain Adaptation for Sim2Real Hinge Joint Tracking with Flexible Sensors.
Jiawei Fang, Haishan Song, Chengxu Zuo, Xiaoxia Gao, Xiaowei Chen, Shihui Guo, Yipeng Qin
2024Sub-token ViT Embedding via Stochastic Resonance Transformers.
Dong Lao, Yangchao Wu, Tian Yu Liu, Alex Wong, Stefano Soatto
2024Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments.
Runfa Chen, Ling Wang, Yu Du, Tianrui Xue, Fuchun Sun, Jianwei Zhang, Wenbing Huang
2024Subgoal-based Demonstration Learning for Formal Theorem Proving.
Xueliang Zhao, Wenda Li, Lingpeng Kong
2024Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products.
Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron
2024Subhomogeneous Deep Equilibrium Models.
Pietro Sittoni, Francesco Tudisco
2024Submodular framework for structured-sparse optimal transport.
Piyushi Manupriya, Pratik Jawanpuria, Karthik S. Gurumoorthy, Saketha Nath Jagarlapudi, Bamdev Mishra
2024Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation.
Ossi Räisä, Joonas Jälkö, Antti Honkela
2024Successor Features for Efficient Multi-Subject Controlled Text Generation.
Meng Cao, Mehdi Fatemi, Jackie C. K. Cheung, Samira Shabanian
2024Superpoint Gaussian Splatting for Real-Time High-Fidelity Dynamic Scene Reconstruction.
Diwen Wan, Ruijie Lu, Gang Zeng
2024Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation.
Thomas Merth, Qichen Fu, Mohammad Rastegari, Mahyar Najibi
2024Supervised Matrix Factorization: Local Landscape Analysis and Applications.
Joowon Lee, Hanbaek Lyu, Weixin Yao
2024SurfPro: Functional Protein Design Based on Continuous Surface.
Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin
2024Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces.
Fang Wu, Stan Z. Li
2024Surprisingly Strong Performance Prediction with Neural Graph Features.
Gabriela Kadlecová, Jovita Lukasik, Martin Pilát, Petra Vidnerová, Mahmoud Safari, Roman Neruda, Frank Hutter
2024Swallowing the Bitter Pill: Simplified Scalable Conformer Generation.
Yuyang Wang, Ahmed A. A. Elhag, Navdeep Jaitly, Joshua M. Susskind, Miguel Ángel Bautista
2024Switchable Decision: Dynamic Neural Generation Networks.
Shujian Zhang, Korawat Tanwisuth, Chengyue Gong, Pengcheng He, Mingyuan Zhou
2024Switched Flow Matching: Eliminating Singularities via Switching ODEs.
Qunxi Zhu, Wei Lin
2024Switching the Loss Reduces the Cost in Batch Reinforcement Learning.
Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári
2024SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment.
Ziping Ma, Furong Xu, Jian Liu, Ming Yang, Qingpei Guo
2024Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion.
Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli Shama Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue
2024Symmetric Matrix Completion with ReLU Sampling.
Huikang Liu, Peng Wang, Longxiu Huang, Qing Qu, Laura Balzano
2024Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization.
Hyeonah Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park
2024Symmetry Induces Structure and Constraint of Learning.
Liu Ziyin
2024Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs.
Mingyu Kim, Jun-Seong Kim, Se-Young Yun, Jin-Hwa Kim
2024SΩI: Score-based O-INFORMATION Estimation.
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
2024TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision.
Zhuo Chen, Jacob McCarran, Esteban Vizcaino, Marin Soljacic, Di Luo
2024TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors.
Yichuan Mo, Hui Huang, Mingjie Li, Ang Li, Yisen Wang
2024TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression.
Megh Shukla, Mathieu Salzmann, Alexandre Alahi
2024TSLANet: Rethinking Transformers for Time Series Representation Learning.
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Xiaoli Li
2024TVE: Learning Meta-attribution for Transferable Vision Explainer.
Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du, Chia-Yuan Chang, Shaochen Zhong, Zirui Liu, Zhaozhuo Xu, Kaixiong Zhou, Xuanting Cai, Xia Hu
2024TabLog: Test-Time Adaptation for Tabular Data Using Logic Rules.
Weijieying Ren, Xiaoting Li, Huiyuan Chen, Vineeth Rakesh, Zhuoyi Wang, Mahashweta Das, Vasant G. Honavar
2024Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images.
Jun-Peng Jiang, Han-Jia Ye, Leye Wang, Yang Yang, Yuan Jiang, De-Chuan Zhan
2024Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation.
Wanpeng Zhang, Yilin Li, Boyu Yang, Zongqing Lu
2024Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More.
Fanchen Bu, Hyeonsoo Jo, Soo Yong Lee, Sungsoo Ahn, Kijung Shin
2024Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains.
Junhong Shen, Neil A. Tenenholtz, James Brian Hall, David Alvarez-Melis, Nicolò Fusi
2024Tandem Transformers for Inference Efficient LLMs.
Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
2024Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation.
Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai, Ramki Gummadi, Oscar A. Ramirez, Christopher K. Harris, A. Rupam Mahmood, Dale Schuurmans
2024Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models.
Yongxian Wei, Zixuan Hu, Li Shen, Zhenyi Wang, Yu Li, Chun Yuan, Dacheng Tao
2024Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning.
Yusong Hu, De Cheng, Dingwen Zhang, Nannan Wang, Tongliang Liu, Xinbo Gao
2024Taylor Videos for Action Recognition.
Lei Wang, Xiuyuan Yuan, Tom Gedeon, Liang Zheng
2024Tell, Don't Show: Language Guidance Eases Transfer Across Domains in Images and Videos.
Tarun Kalluri, Bodhisattwa Prasad Majumder, Manmohan Chandraker
2024Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning.
Zijian Guo, Weichao Zhou, Wenchao Li
2024Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning.
Mingqing Xiao, Yixin Zhu, Di He, Zhouchen Lin
2024Test-Time Degradation Adaptation for Open-Set Image Restoration.
Yuanbiao Gou, Haiyu Zhao, Boyun Li, Xinyan Xiao, Xi Peng
2024Test-Time Model Adaptation with Only Forward Passes.
Shuaicheng Niu, Chunyan Miao, Guohao Chen, Pengcheng Wu, Peilin Zhao
2024Test-Time Regret Minimization in Meta Reinforcement Learning.
Mirco Mutti, Aviv Tamar
2024Testing the Feasibility of Linear Programs with Bandit Feedback.
Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott
2024The Balanced-Pairwise-Affinities Feature Transform.
Daniel Shalam, Simon Korman
2024The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents.
Yatin Dandi, Emanuele Troiani, Luca Arnaboldi, Luca Pesce, Lenka Zdeborová, Florent Krzakala
2024The Computational Complexity of Finding Second-Order Stationary Points.
Andreas Kontogiannis, Vasilis Pollatos, Sotiris Kanellopoulos, Panayotis Mertikopoulos, Aris Pagourtzis, Ioannis Panageas
2024The Effect of Weight Precision on the Neuron Count in Deep ReLU Networks.
Songhua He, Periklis A. Papakonstantinou
2024The Emergence of Reproducibility and Consistency in Diffusion Models.
Huijie Zhang, Jinfan Zhou, Yifu Lu, Minzhe Guo, Peng Wang, Liyue Shen, Qing Qu
2024The Entropy Enigma: Success and Failure of Entropy Minimization.
Ori Press, Ravid Shwartz-Ziv, Yann LeCun, Matthias Bethge
2024The Expressive Power of Path-Based Graph Neural Networks.
Caterina Graziani, Tamara Drucks, Fabian Jogl, Monica Bianchini, Franco Scarselli, Thomas Gärtner
2024The Fundamental Limits of Least-Privilege Learning.
Theresa Stadler, Bogdan Kulynych, Michael Gastpar, Nicolas Papernot, Carmela Troncoso
2024The Good, The Bad, and Why: Unveiling Emotions in Generative AI.
Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Xinyi Wang, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie
2024The Illusion of State in State-Space Models.
William Merrill, Jackson Petty, Ashish Sabharwal
2024The Linear Representation Hypothesis and the Geometry of Large Language Models.
Kiho Park, Yo Joong Choe, Victor Veitch
2024The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm.
Giseung Park, Woohyeon Byeon, Seongmin Kim, Elad Havakuk, Amir Leshem, Youngchul Sung
2024The Merit of River Network Topology for Neural Flood Forecasting.
Nikolas Kirschstein, Yixuan Sun
2024The Non-linear F-Design and Applications to Interactive Learning.
Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang
2024The Perception-Robustness Tradeoff in Deterministic Image Restoration.
Guy Ohayon, Tomer Michaeli, Michael Elad
2024The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks.
Ziquan Liu, Yufei Cui, Yan Yan, Yi Xu, Xiangyang Ji, Xue Liu, Antoni B. Chan
2024The Pitfalls of Next-Token Prediction.
Gregor Bachmann, Vaishnavh Nagarajan
2024The Privacy Power of Correlated Noise in Decentralized Learning.
Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi, Martin Jaggi, Rachid Guerraoui
2024The Relative Value of Prediction in Algorithmic Decision Making.
Juan Carlos Perdomo
2024The Role of Learning Algorithms in Collective Action.
Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal
2024The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright BreachesWithout Adjusting Finetuning Pipeline.
Haonan Wang, Qianli Shen, Yao Tong, Yang Zhang, Kenji Kawaguchi
2024The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling.
Jiajun Ma, Shuchen Xue, Tianyang Hu, Wenjia Wang, Zhaoqiang Liu, Zhenguo Li, Zhi-Ming Ma, Kenji Kawaguchi
2024The WMDP Benchmark: Measuring and Reducing Malicious Use with Unlearning.
Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D. Li, Ann-Kathrin Dombrowski, Shashwat Goel, Gabriel Mukobi, Nathan Helm-Burger, Rassin Lababidi, Lennart Justen, Andrew B. Liu, Michael Chen, Isabelle Barrass, Oliver Zhang, Xiaoyuan Zhu, Rishub Tamirisa, Bhrugu Bharathi, Ariel Herbert-Voss, Cort B. Breuer, Andy Zou, Mantas Mazeika, Zifan Wang, Palash Oswal, Weiran Lin, Adam A. Hunt, Justin Tienken-Harder, Kevin Y. Shih, Kemper Talley, John Guan, Ian Steneker, David Campbell, Brad Jokubaitis, Steven Basart, Stephen Fitz, Ponnurangam Kumaraguru, Kallol Krishna Karmakar, Uday Kiran Tupakula, Vijay Varadharajan, Yan Shoshitaishvili, Jimmy Ba, Kevin M. Esvelt, Alexandr Wang, Dan Hendrycks
2024Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability.
Sepanta Zeighami, Cyrus Shahabi
2024Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians.
Tom Huix, Anna Korba, Alain Oliviero Durmus, Eric Moulines
2024Theoretical insights for diffusion guidance: A case study for Gaussian mixture models.
Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, Yuting Wei
2024Theory of Consistency Diffusion Models: Distribution Estimation Meets Fast Sampling.
Zehao Dou, Minshuo Chen, Mengdi Wang, Zhuoran Yang
2024Thermometer: Towards Universal Calibration for Large Language Models.
Maohao Shen, Subhro Das, Kristjan H. Greenewald, Prasanna Sattigeri, Gregory W. Wornell, Soumya Ghosh
2024Think Before You Act: Decision Transformers with Working Memory.
Jikun Kang, Romain Laroche, Xingdi Yuan, Adam Trischler, Xue Liu, Jie Fu
2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders.
Zhiheng Zhang
2024Tilt and Average : Geometric Adjustment of the Last Layer for Recalibration.
Gyusang Cho, Chan-Hyun Youn
2024Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers.
Johann Schmidt, Sebastian Stober
2024Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks.
Stefano Sarao Mannelli, Yaraslau Ivashinka, Andrew M. Saxe, Luca Saglietti
2024Time Series Diffusion in the Frequency Domain.
Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar
2024Time Weaver: A Conditional Time Series Generation Model.
Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep P. Chinchali
2024Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning.
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, B. Aditya Prakash
2024TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning.
Xiwen Chen, Peijie Qiu, Wenhui Zhu, Huayu Li, Hao Wang, Aristeidis Sotiras, Yalin Wang, Abolfazl Razi
2024TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling.
Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, Mingsheng Long
2024TimeX++: Learning Time-Series Explanations with Information Bottleneck.
Zichuan Liu, Tianchun Wang, Jimeng Shi, Xu Zheng, Zhuomin Chen, Lei Song, Wenqian Dong, Jayantha Obeysekera, Farhad Shirani, Dongsheng Luo
2024Timer: Generative Pre-trained Transformers Are Large Time Series Models.
Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long
2024TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge.
Young D. Kwon, Rui Li, Stylianos I. Venieris, Jagmohan Chauhan, Nicholas Donald Lane, Cecilia Mascolo
2024To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO.
Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang
2024To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models.
George-Octavian Barbulescu, Peter Triantafillou
2024To the Max: Reinventing Reward in Reinforcement Learning.
Grigorii Veviurko, Wendelin Boehmer, Mathijs de Weerdt
2024Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models.
Mingjia Huo, Sai Ashish Somayajula, Youwei Liang, Ruisi Zhang, Farinaz Koushanfar, Pengtao Xie
2024Token-level Direct Preference Optimization.
Yongcheng Zeng, Guoqing Liu, Weiyu Ma, Ning Yang, Haifeng Zhang, Jun Wang
2024Topological Neural Networks go Persistent, Equivariant, and Continuous.
Yogesh Verma, Amauri H. Souza, Vikas Garg
2024Total Variation Distance Meets Probabilistic Inference.
Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran
2024Total Variation Floodgate for Variable Importance Inference in Classification.
Wenshuo Wang, Lucas Janson, Lihua Lei, Aaditya Ramdas
2024Toward Adaptive Reasoning in Large Language Models with Thought Rollback.
Sijia Chen, Baochun Li
2024Toward Availability Attacks in 3D Point Clouds.
Yifan Zhu, Yibo Miao, Yinpeng Dong, Xiao-Shan Gao
2024Towards AutoAI: Optimizing a Machine Learning System with Black-box and Differentiable Components.
Zhiliang Chen, Chuan-Sheng Foo, Bryan Kian Hsiang Low
2024Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention.
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma
2024Towards Certified Unlearning for Deep Neural Networks.
Binchi Zhang, Yushun Dong, Tianhao Wang, Jundong Li
2024Towards Compositionality in Concept Learning.
Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong
2024Towards Efficient Exact Optimization of Language Model Alignment.
Haozhe Ji, Cheng Lu, Yilin Niu, Pei Ke, Hongning Wang, Jun Zhu, Jie Tang, Minlie Huang
2024Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration.
Zhengyang Zhuge, Peisong Wang, Xingting Yao, Jian Cheng
2024Towards Efficient Training and Evaluation of Robust Models against l0 Bounded Adversarial Perturbations.
Xuyang Zhong, Yixiao Huang, Chen Liu
2024Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph.
Yufei Kuang, Jie Wang, Yuyan Zhou, Xijun Li, Fangzhou Zhu, Jianye Hao, Feng Wu
2024Towards General Neural Surrogate Solvers with Specialized Neural Accelerators.
Chenkai Mao, Robert Lupoiu, Tianxiang Dai, Mingkun Chen, Jonathan A. Fan
2024Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective.
Yuxin Dong, Tieliang Gong, Hong Chen, Zhongjiang He, Mengxiang Li, Shuangyong Song, Chen Li
2024Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles.
Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Dinesh Manocha, Amrit S. Bedi
2024Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation.
Yibo Yang, Xiaojie Li, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Adel Bibi, Philip Torr, Bernard Ghanem
2024Towards Modular LLMs by Building and Reusing a Library of LoRAs.
Oleksiy Ostapenko, Zhan Su, Edoardo M. Ponti, Laurent Charlin, Nicolas Le Roux, Lucas Caccia, Alessandro Sordoni
2024Towards Neural Architecture Search through Hierarchical Generative Modeling.
Lichuan Xiang, Lukasz Dudziak, Mohamed S. Abdelfattah, Abhinav Mehrotra, Nicholas Donald Lane, Hongkai Wen
2024Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error.
Haoran Li, Zicheng Zhang, Wang Luo, Congying Han, Yudong Hu, Tiande Guo, Shichen Liao
2024Towards Realistic Model Selection for Semi-supervised Learning.
Muyang Li, Xiaobo Xia, Runze Wu, Fengming Huang, Jun Yu, Bo Han, Tongliang Liu
2024Towards Resource-friendly, Extensible and Stable Incomplete Multi-view Clustering.
Shengju Yu, Zhibin Dong, Siwei Wang, Xinhang Wan, Yue Liu, Weixuan Liang, Pei Zhang, Wenxuan Tu, Xinwang Liu
2024Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption.
Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang
2024Towards Scalable and Versatile Weight Space Learning.
Konstantin Schürholt, Michael W. Mahoney, Damian Borth
2024Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features.
Chao Wang, Xin Bing, Xin He, Caixing Wang
2024Towards Theoretical Understandings of Self-Consuming Generative Models.
Shi Fu, Sen Zhang, Yingjie Wang, Xinmei Tian, Dacheng Tao
2024Towards Understanding Inductive Bias in Transformers: A View From Infinity.
Itay Lavie, Guy Gur-Ari, Zohar Ringel
2024Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features.
Simone Bombari, Marco Mondelli
2024Towards Unified Multi-granularity Text Detection with Interactive Attention.
Xingyu Wan, Chengquan Zhang, Pengyuan Lyu, Sen Fan, Zihan Ni, Kun Yao, Errui Ding, Jingdong Wang
2024Towards a Better Theoretical Understanding of Independent Subnetwork Training.
Egor Shulgin, Peter Richtárik
2024Towards a Self-contained Data-driven Global Weather Forecasting Framework.
Yi Xiao, Lei Bai, Wei Xue, Hao Chen, Kun Chen, Kang Chen, Tao Han, Wanli Ouyang
2024Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model.
Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert P. Dick, Ekdeep Singh Lubana, Hidenori Tanaka
2024Towards efficient deep spiking neural networks construction with spiking activity based pruning.
Yaxin Li, Qi Xu, Jiangrong Shen, Hongming Xu, Long Chen, Gang Pan
2024Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms.
Ye Tian, Haolei Weng, Yang Feng
2024Trainable Transformer in Transformer.
Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, Sanjeev Arora
2024Trained Random Forests Completely Reveal your Dataset.
Julien Ferry, Ricardo Fukasawa, Timothée Pascal, Thibaut Vidal
2024Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization.
Deokjae Lee, Hyun Oh Song, Kyunghyun Cho
2024Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning.
Zhiheng Xi, Wenxiang Chen, Boyang Hong, Senjie Jin, Rui Zheng, Wei He, Yiwen Ding, Shichun Liu, Xin Guo, Junzhe Wang, Honglin Guo, Wei Shen, Xiaoran Fan, Yuhao Zhou, Shihan Dou, Xiao Wang, Xinbo Zhang, Peng Sun, Tao Gui, Qi Zhang, Xuanjing Huang
2024Training-Free Long-Context Scaling of Large Language Models.
Chenxin An, Fei Huang, Jun Zhang, Shansan Gong, Xipeng Qiu, Chang Zhou, Lingpeng Kong
2024Transferable Facial Privacy Protection against Blind Face Restoration via Domain-Consistent Adversarial Obfuscation.
Kui Zhang, Hang Zhou, Jie Zhang, Wenbo Zhou, Weiming Zhang, Nenghai Yu
2024Transferring Knowledge From Large Foundation Models to Small Downstream Models.
Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Bernie Wang, Andrew Gordon Wilson
2024Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models.
Akhil Kedia, Mohd Abbas Zaidi, Sushil Khyalia, JungHo Jung, Harshith Goka, Haejun Lee
2024Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context.
Xiang Cheng, Yuxin Chen, Suvrit Sra
2024Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape.
Juno Kim, Taiji Suzuki
2024Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot.
Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee
2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.
Tri Dao, Albert Gu
2024Transformers, parallel computation, and logarithmic depth.
Clayton Sanford, Daniel Hsu, Matus Telgarsky
2024Transforming and Combining Rewards for Aligning Large Language Models.
Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alexander Nicholas D'Amour, Sanmi Koyejo, Victor Veitch
2024Transitional Uncertainty with Layered Intermediate Predictions.
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
2024Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning.
Dongkwan Kim, Alice Oh
2024Translation Equivariant Transformer Neural Processes.
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim, Lakee Sivaraya, Stratis Markou, James Requeima, Wessel P. Bruinsma, Richard E. Turner
2024Transolver: A Fast Transformer Solver for PDEs on General Geometries.
Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, Mingsheng Long
2024Transport of Algebraic Structure to Latent Embeddings.
Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
2024TravelPlanner: A Benchmark for Real-World Planning with Language Agents.
Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, Yu Su
2024Triadic-OCD: Asynchronous Online Change Detection with Provable Robustness, Optimality, and Convergence.
Yancheng Huang, Kai Yang, Zelin Zhu, Leian Chen
2024Triple Changes Estimator for Targeted Policies.
Sina Akbari, Negar Kiyavash
2024Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers.
Md. Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
2024Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning.
Kyle Hsu, Jubayer Ibn Hamid, Kaylee Burns, Chelsea Finn, Jiajun Wu
2024TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks.
Zhiruo Wang, Graham Neubig, Daniel Fried
2024Truly No-Regret Learning in Constrained MDPs.
Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi, Niao He
2024Trust Regions for Explanations via Black-Box Probabilistic Certification.
Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy
2024Trust the Model Where It Trusts Itself - Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption.
Bernd Frauenknecht, Artur Eisele, Devdutt Subhasish, Friedrich Solowjow, Sebastian Trimpe
2024Trustless Audits without Revealing Data or Models.
Suppakit Waiwitlikhit, Ion Stoica, Yi Sun, Tatsunori Hashimoto, Daniel Kang
2024Trustworthy Actionable Perturbations.
Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon
2024Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning.
Zongmeng Zhang, Yufeng Shi, Jinhua Zhu, Wengang Zhou, Xiang Qi, Peng Zhang, Houqiang Li
2024Tuning-Free Stochastic Optimization.
Ahmed Khaled, Chi Jin
2024Tuning-free Estimation and Inference of Cumulative Distribution Function under Local Differential Privacy.
Yi Liu, Qirui Hu, Linglong Kong
2024Turnstile ℓp leverage score sampling with applications.
Alexander Munteanu, Simon Omlor
2024Two Fists, One Heart: Multi-Objective Optimization Based Strategy Fusion for Long-tailed Learning.
Zhe Zhao, Pengkun Wang, Haibin Wen, Wei Xu, Song Lai, Qingfu Zhang, Yang Wang
2024Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness.
Guibin Zhang, Yanwei Yue, Kun Wang, Junfeng Fang, Yongduo Sui, Kai Wang, Yuxuan Liang, Dawei Cheng, Shirui Pan, Tianlong Chen
2024Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection.
Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, Yingyu Liang, Somesh Jha
2024Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation.
Zhenyu He, Guhao Feng, Shengjie Luo, Kai Yang, Liwei Wang, Jingjing Xu, Zhi Zhang, Hongxia Yang, Di He
2024Two Tales of Single-Phase Contrastive Hebbian Learning.
Rasmus Kjær Høier, Christopher Zach
2024Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias.
Baohong Li, Anpeng Wu, Ruoxuan Xiong, Kun Kuang
2024Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints.
Yuantong Li, Guang Cheng, Xiaowu Dai
2024Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data.
Haitong Liu, Qiang Li, Hoi-To Wai
2024UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs.
Xi Han, Fei Hou, Hong Qin
2024ULAREF: A Unified Label Refinement Framework for Learning with Inaccurate Supervision.
Congyu Qiao, Ning Xu, Yihao Hu, Xin Geng
2024ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback.
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Bingxiang He, Wei Zhu, Yuan Ni, Guotong Xie, Ruobing Xie, Yankai Lin, Zhiyuan Liu, Maosong Sun
2024UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis.
Yunhao Zhang, Minghao Liu, Shengyang Zhou, Junchi Yan
2024UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers.
Duo Peng, Qiuhong Ke, Jun Liu
2024UPOCR: Towards Unified Pixel-Level OCR Interface.
Dezhi Peng, Zhenhua Yang, Jiaxin Zhang, Chongyu Liu, Yongxin Shi, Kai Ding, Fengjun Guo, Lianwen Jin
2024USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval.
Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Wittawat Jitkrittum, Veeranjaneyulu Sadhanala, Sadeep Jayasumana, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar
2024Unbiased Multi-Label Learning from Crowdsourced Annotations.
Mingxuan Xia, Zenan Huang, Runze Wu, Gengyu Lyu, Junbo Zhao, Gang Chen, Haobo Wang
2024Uncertainty Estimation by Density Aware Evidential Deep Learning.
Taeseong Yoon, Heeyoung Kim
2024Uncertainty for Active Learning on Graphs.
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
2024Uncertainty-Aware Reward-Free Exploration with General Function Approximation.
Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu
2024Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise.
Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook, Yan Dai
2024Understanding Diffusion Models by Feynman's Path Integral.
Yuji Hirono, Akinori Tanaka, Kenji Fukushima
2024Understanding Finetuning for Factual Knowledge Extraction.
Gaurav Rohit Ghosal, Tatsunori Hashimoto, Aditi Raghunathan
2024Understanding Forgetting in Continual Learning with Linear Regression.
Meng Ding, Kaiyi Ji, Di Wang, Jinhui Xu
2024Understanding Heterophily for Graph Neural Networks.
Junfu Wang, Yuanfang Guo, Liang Yang, Yunhong Wang
2024Understanding Inter-Concept Relationships in Concept-Based Models.
Naveen Raman, Mateo Espinosa Zarlenga, Mateja Jamnik
2024Understanding MLP-Mixer as a wide and sparse MLP.
Tomohiro Hayase, Ryo Karakida
2024Understanding Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation.
Xinyi Wang, Alfonso Amayuelas, Kexun Zhang, Liangming Pan, Wenhu Chen, William Yang Wang
2024Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models.
Yifei Ming, Yixuan Li
2024Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation.
Haibo Yang, Peiwen Qiu, Prashant Khanduri, Minghong Fang, Jia Liu
2024Understanding Stochastic Natural Gradient Variational Inference.
Kaiwen Wu, Jacob R. Gardner
2024Understanding Unimodal Bias in Multimodal Deep Linear Networks.
Yedi Zhang, Peter E. Latham, Andrew M. Saxe
2024Understanding and Diagnosing Deep Reinforcement Learning.
Ezgi Korkmaz
2024Understanding the Effects of Iterative Prompting on Truthfulness.
Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
2024Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error.
Masaaki Nishino, Kengo Nakamura, Norihito Yasuda
2024Understanding the Learning Dynamics of Alignment with Human Feedback.
Shawn Im, Yixuan Li
2024Understanding the Training Speedup from Sampling with Approximate Losses.
Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi
2024UniAudio: Towards Universal Audio Generation with Large Language Models.
Dongchao Yang, Jinchuan Tian, Xu Tan, Rongjie Huang, Songxiang Liu, Haohan Guo, Xuankai Chang, Jiatong Shi, Sheng Zhao, Jiang Bian, Zhou Zhao, Xixin Wu, Helen M. Meng
2024UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning.
Shikun Feng, Yuyan Ni, Minghao Li, Yanwen Huang, Zhi-Ming Ma, Wei-Ying Ma, Yanyan Lan
2024Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding.
Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian J. McAuley, Zichao Yang, Eric P. Xing, Zhiting Hu
2024Unified Training of Universal Time Series Forecasting Transformers.
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
2024Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models.
Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao, Han Liu
2024Uniformly Stable Algorithms for Adversarial Training and Beyond.
Jiancong Xiao, Jiawei Zhang, Zhi-Quan Luo, Asuman E. Ozdaglar
2024Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations.
Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li
2024Unifying Image Processing as Visual Prompting Question Answering.
Yihao Liu, Xiangyu Chen, Xianzheng Ma, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong
2024Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes.
Hyunouk Ko, Xiaoming Huo
2024Universal Gradient Methods for Stochastic Convex Optimization.
Anton Rodomanov, Ali Kavis, Yongtao Wu, Kimon Antonakopoulos, Volkan Cevher
2024Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues.
Antonio Orvieto, Soham De, Caglar Gulcehre, Razvan Pascanu, Samuel L. Smith
2024Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts.
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei
2024Unlock the Cognitive Generalization of Deep Reinforcement Learning via Granular Ball Representation.
Jiashun Liu, Jianye Hao, Yi Ma, Shuyin Xia
2024Unlocking the Power of Spatial and Temporal Information in Medical Multimodal Pre-training.
Jinxia Yang, Bing Su, Xin Zhao, Ji-Rong Wen
2024Unmasking Vulnerabilities: Cardinality Sketches under Adaptive Inputs.
Sara Ahmadian, Edith Cohen
2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning.
Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu
2024Unsupervised Concept Discovery Mitigates Spurious Correlations.
Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi
2024Unsupervised Domain Adaptation for Anatomical Structure Detection in Ultrasound Images.
Bin Pu, Xingguo Lv, Jiewen Yang, Guannan He, Xingbo Dong, Yiqun Lin, Shengli Li, Tan Ying, Fei Liu, Ming Chen, Zhe Jin, Kenli Li, Xiaomeng Li
2024Unsupervised Episode Generation for Graph Meta-learning.
Jihyeong Jung, Sangwoo Seo, Sungwon Kim, Chanyoung Park
2024Unsupervised Evaluation of Code LLMs with Round-Trip Correctness.
Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin
2024Unsupervised Parameter-free Simplicial Representation Learning with Scattering Transforms.
Hiren Madhu, Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri
2024Unsupervised Representation Learning of Brain Activity via Bridging Voxel Activity and Functional Connectivity.
Ali Behrouz, Parsa Delavari, Farnoosh Hashemi
2024Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings.
Kevin Frans, Seohong Park, Pieter Abbeel, Sergey Levine
2024Unveiling Privacy, Memorization, and Input Curvature Links.
Deepak Ravikumar, Efstathia Soufleri, Abolfazl Hashemi, Kaushik Roy
2024Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration.
Zhongzhi Yu, Zheng Wang, Yonggan Fu, Huihong Shi, Khalid Shaikh, Yingyan Celine Lin
2024Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression.
Zhankun Luo, Abolfazl Hashemi
2024Unveiling the Dynamics of Information Interplay in Supervised Learning.
Kun Song, Zhiquan Tan, Bochao Zou, Huimin Ma, Weiran Huang
2024Unveiling the Potential of AI for Nanomaterial Morphology Prediction.
Ivan Dubrovsky, Andrei Dmitrenko, Aleksei Dmitrenko, Nikita Serov, Vladimir Vinogradov
2024Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers.
Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
2024Using AI Uncertainty Quantification to Improve Human Decision-Making.
Laura Marusich, Jonathan Z. Bakdash, Yan Zhou, Murat Kantarcioglu
2024Using Left and Right Brains Together: Towards Vision and Language Planning.
Jun Cen, Chenfei Wu, Xiao Liu, Shengming Yin, Yixuan Pei, Jinglong Yang, Qifeng Chen, Nan Duan, Jianguo Zhang
2024Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs.
S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang
2024VITS : Variational Inference Thompson Sampling for contextual bandits.
Pierre Clavier, Tom Huix, Alain Oliviero Durmus
2024VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees.
Anahita Baninajjar, Ahmed Rezine, Amir Aminifar
2024VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling.
Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li
2024Vague Prototype-Oriented Diffusion Model for Multi-Class Anomaly Detection.
Yuxin Li, Yaoxuan Feng, Bo Chen, Wenchao Chen, Yubiao Wang, Xinyue Hu, Baolin Sun, Chunhui Qu, Mingyuan Zhou
2024Value-Evolutionary-Based Reinforcement Learning.
Pengyi Li, Jianye Hao, Hongyao Tang, Yan Zheng, Fazl Barez
2024Vanilla Bayesian Optimization Performs Great in High Dimensions.
Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi
2024Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models.
Tanmay Gautam, Youngsuk Park, Hao Zhou, Parameswaran Raman, Wooseok Ha
2024Variational Inference with Coverage Guarantees in Simulation-Based Inference.
Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari
2024Variational Learning is Effective for Large Deep Networks.
Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
2024Variational Linearized Laplace Approximation for Bayesian Deep Learning.
Luis A. Ortega Andrés, Simón Rodríguez Santana, Daniel Hernández-Lobato
2024Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts.
Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee
2024Variational Schrödinger Diffusion Models.
Wei Deng, Weijian Luo, Yixin Tan, Marin Bilos, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen
2024Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention.
Zhen Qin, Weigao Sun, Dong Li, Xuyang Shen, Weixuan Sun, Yiran Zhong
2024Vector Quantization Pretraining for EEG Time Series with Random Projection and Phase Alignment.
Haokun Gui, Xiucheng Li, Xinyang Chen
2024Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations.
Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp, Mathias Niepert
2024Verification of Machine Unlearning is Fragile.
Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li
2024Verifying message-passing neural networks via topology-based bounds tightening.
Christopher Hojny, Shiqiang Zhang, Juan S. Campos, Ruth Misener
2024ViP: A Differentially Private Foundation Model for Computer Vision.
Yaodong Yu, Maziar Sanjabi, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization.
Yang Jin, Zhicheng Sun, Kun Xu, Liwei Chen, Hao Jiang, Quzhe Huang, Chengru Song, Yuliang Liu, Di Zhang, Yang Song, Kun Gai, Yadong Mu
2024Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition.
Hao Fei, Shengqiong Wu, Wei Ji, Hanwang Zhang, Meishan Zhang, Mong-Li Lee, Wynne Hsu
2024VideoPoet: A Large Language Model for Zero-Shot Video Generation.
Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A. Ross, Bryan Seybold, Lu Jiang
2024VideoPrism: A Foundational Visual Encoder for Video Understanding.
Long Zhao, Nitesh Bharadwaj Gundavarapu, Liangzhe Yuan, Hao Zhou, Shen Yan, Jennifer J. Sun, Luke Friedman, Rui Qian, Tobias Weyand, Yue Zhao, Rachel Hornung, Florian Schroff, Ming-Hsuan Yang, David A. Ross, Huisheng Wang, Hartwig Adam, Mikhail Sirotenko, Ting Liu, Boqing Gong
2024Viewing Transformers Through the Lens of Long Convolutions Layers.
Itamar Zimerman, Lior Wolf
2024VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception.
Zhaoliang Wan, Yonggen Ling, Senlin Yi, Lu Qi, Wang Wei Lee, Minglei Lu, Sicheng Yang, Xiao Teng, Peng Lu, Xu Yang, Ming-Hsuan Yang, Hui Cheng
2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.
Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, Xinggang Wang
2024Vision Transformers as Probabilistic Expansion from Learngene.
Qiufeng Wang, Xu Yang, Haokun Chen, Xin Geng
2024VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context.
Yunxin Li, Baotian Hu, Haoyuan Shi, Wei Wang, Longyue Wang, Min Zhang
2024Visual Representation Learning with Stochastic Frame Prediction.
Huiwon Jang, Dongyoung Kim, Junsu Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo
2024Visual Transformer with Differentiable Channel Selection: An Information Bottleneck Inspired Approach.
Yancheng Wang, Ping Li, Yingzhen Yang
2024Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models.
Jinhao Li, Haopeng Li, Sarah Monazam Erfani, Lei Feng, James Bailey, Feng Liu
2024Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions.
Yongqiang Cai
2024VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model.
Pengying Wu, Yao Mu, Bingxian Wu, Yi Hou, Ji Ma, Shanghang Zhang, Chang Liu
2024WARM: On the Benefits of Weight Averaged Reward Models.
Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, Johan Ferret
2024WAVES: Benchmarking the Robustness of Image Watermarks.
Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang
2024WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer.
Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh A. P., Vaibhav Rajan
2024Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer.
Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak, Tal Nawy, Anna Gilbert, Dana Pe'er
2024Watermark Stealing in Large Language Models.
Nikola Jovanovic, Robin Staab, Martin T. Vechev
2024Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models.
Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, Boaz Barak
2024Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeffrey Wu
2024Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation.
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb
2024Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation.
Pei Liu, Luping Ji
2024WebLINX: Real-World Website Navigation with Multi-Turn Dialogue.
Xing Han Lù, Zdenek Kasner, Siva Reddy
2024Weighted distance nearest neighbor condensing.
Lee-Ad Gottlieb, Timor Sharabi, Roi Weiss
2024Weisfeiler Leman for Euclidean Equivariant Machine Learning.
Snir Hordan, Tal Amir, Nadav Dym
2024Weisfeiler-Leman at the margin: When more expressivity matters.
Billy Joe Franks, Christopher Morris, Ameya Velingker, Floris Geerts
2024What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks.
Xingwu Chen, Difan Zou
2024What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding.
Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, Zaixi Zhang, Pin-Yu Chen
2024What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement.
Xisen Jin, Xiang Ren
2024What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks.
Ching-Yun Ko, Pin-Yu Chen, Payel Das, Jeet Mohapatra, Luca Daniel
2024What is Dataset Distillation Learning?
William Yang, Ye Zhu, Zhiwei Deng, Olga Russakovsky
2024What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis.
Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
2024What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation.
Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, Andrew M. Saxe
2024What's the score? Automated Denoising Score Matching for Nonlinear Diffusions.
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
2024When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions.
Zhening Li, Gabriel Poesia, Armando Solar-Lezama
2024When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models.
Haoran You, Yichao Fu, Zheng Wang, Amir Yazdanbakhsh, Yingyan Celine Lin
2024When Representations Align: Universality in Representation Learning Dynamics.
Loek van Rossem, Andrew M. Saxe
2024When Will Gradient Regularization Be Harmful?
Yang Zhao, Hao Zhang, Xiuyuan Hu
2024When and How Does In-Distribution Label Help Out-of-Distribution Detection?
Xuefeng Du, Yiyou Sun, Yixuan Li
2024When is Transfer Learning Possible?
My Phan, Kianté Brantley, Stephanie Milani, Soroush Mehri, Gokul Swamy, Geoffrey J. Gordon
2024Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning.
Yuxiao Wen, Arthur Jacot
2024Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models.
Xavier Suau, Pieter Delobelle, Katherine Metcalf, Armand Joulin, Nicholas Apostoloff, Luca Zappella, Pau Rodríguez
2024Why Do Animals Need Shaping? A Theory of Task Composition and Curriculum Learning.
Jin Hwa Lee, Stefano Sarao Mannelli, Andrew M. Saxe
2024Why Do You Grok? A Theoretical Analysis on Grokking Modular Addition.
Mohamad Amin Mohamadi, Zhiyuan Li, Lei Wu, Danica J. Sutherland
2024Why Larger Language Models Do In-context Learning Differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu, Yingyu Liang
2024Why do Variational Autoencoders Really Promote Disentanglement?
Pratik Bhowal, Achint Soni, Sirisha Rambhatla
2024Winner-takes-all learners are geometry-aware conditional density estimators.
Victor Letzelter, David Perera, Cédric Rommel, Mathieu Fontaine, Slim Essid, Gaël Richard, Patrick Pérez
2024WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vázquez, Nicolas Chapados, Alexandre Lacoste
2024Wukong: Towards a Scaling Law for Large-Scale Recommendation.
Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Shen Li, Yanli Zhao, Yuchen Hao, Yantao Yao, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen
2024X-Oscar: A Progressive Framework for High-quality Text-guided 3D Animatable Avatar Generation.
Yiwei Ma, Zhekai Lin, Jiayi Ji, Yijun Fan, Xiaoshuai Sun, Rongrong Ji
2024Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement.
Che Liu, Zhongwei Wan, Cheng Ouyang, Anand Shah, Wenjia Bai, Rossella Arcucci
2024Zero-Shot Reinforcement Learning via Function Encoders.
Tyler Ingebrand, Amy Zhang, Ufuk Topcu
2024Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion.
Hila Manor, Tomer Michaeli
2024Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach.
Anton Plaksin, Vitaly Kalev
2024Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization.
Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low
2024convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data.
Roman Koshkin, Tomoki Fukai
2024diff History for Neural Language Agents.
Ulyana Piterbarg, Lerrel Pinto, Rob Fergus
2024eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data.
Bo Peng, Xinyi Ling, Ziru Chen, Huan Sun, Xia Ning
2024f-Divergence Based Classification: Beyond the Use of Cross-Entropy.
Nicola Novello, Andrea M. Tonello
2024tinyBenchmarks: evaluating LLMs with fewer examples.
Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin
2024tnGPS: Discovering Unknown Tensor Network Structure Search Algorithms via Large Language Models (LLMs).
Junhua Zeng, Chao Li, Zhun Sun, Qibin Zhao, Guoxu Zhou
2024video-SALMONN: Speech-Enhanced Audio-Visual Large Language Models.
Guangzhi Sun, Wenyi Yu, Changli Tang, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun Ma, Yuxuan Wang, Chao Zhang
2024xT: Nested Tokenization for Larger Context in Large Images.
Ritwik Gupta, Shufan Li, Tyler Zhu, Jitendra Malik, Trevor Darrell, Karttikeya Mangalam
2024ΦFlow: Differentiable Simulations for PyTorch, TensorFlow and Jax.
Philipp Holl, Nils Thuerey