| 2023 | "Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi |
| 2023 | 2D-Shapley: A Framework for Fragmented Data Valuation. Zhihong Liu, Hoang Anh Just, Xiangyu Chang, Xi Chen, Ruoxi Jia |
| 2023 | A Category-theoretical Meta-analysis of Definitions of Disentanglement. Yivan Zhang, Masashi Sugiyama |
| 2023 | A Closer Look at Few-shot Classification Again. Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, Jingkuan Song |
| 2023 | A Closer Look at Self-Supervised Lightweight Vision Transformers. Shaoru Wang, Jin Gao, Zeming Li, Xiaoqin Zhang, Weiming Hu |
| 2023 | A Closer Look at the Intervention Procedure of Concept Bottleneck Models. Sungbin Shin, Yohan Jo, Sungsoo Ahn, Namhoon Lee |
| 2023 | A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests. Bohang Zhang, Guhao Feng, Yiheng Du, Di He, Liwei Wang |
| 2023 | A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging. Jeffrey Wen, Rizwan Ahmad, Philip Schniter |
| 2023 | A Connection between One-Step RL and Critic Regularization in Reinforcement Learning. Benjamin Eysenbach, Matthieu Geist, Sergey Levine, Ruslan Salakhutdinov |
| 2023 | A Coupled Flow Approach to Imitation Learning. Gideon Joseph Freund, Elad Sarafian, Sarit Kraus |
| 2023 | A Critical Revisit of Adversarial Robustness in 3D Point Cloud Recognition with Diffusion-Driven Purification. Jiachen Sun, Jiongxiao Wang, Weili Nie, Zhiding Yu, Zhuoqing Mao, Chaowei Xiao |
| 2023 | A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment. Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei, Jiazhi Li, Mohamed E. Hussein, Wael AbdAlmageed |
| 2023 | A Deep Conjugate Direction Method for Iteratively Solving Linear Systems. Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Alicia Trevino Kala, David Hyde, Joseph Teran |
| 2023 | A Distribution Optimization Framework for Confidence Bounds of Risk Measures. Hao Liang, Zhi-Quan Luo |
| 2023 | A Fast Optimistic Method for Monotone Variational Inequalities. Michael Sedlmayer, Dang-Khoa Nguyen, Radu Ioan Bot |
| 2023 | A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel. Mohamad Amin Mohamadi, Wonho Bae, Danica J. Sutherland |
| 2023 | A Flexible Diffusion Model. Weitao Du, He Zhang, Tao Yang, Yuanqi Du |
| 2023 | A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback. Guanyu Nie, Yididiya Y. Nadew, Yanhui Zhu, Vaneet Aggarwal, Christopher John Quinn |
| 2023 | A Fully First-Order Method for Stochastic Bilevel Optimization. Jeongyeol Kwon, Dohyun Kwon, Stephen Wright, Robert D. Nowak |
| 2023 | A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems. Oliver Slumbers, David Henry Mguni, Stefano B. Blumberg, Stephen Marcus McAleer, Yaodong Yang, Jun Wang |
| 2023 | A General Representation Learning Framework with Generalization Performance Guarantees. Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang |
| 2023 | A Generalization of ViT/MLP-Mixer to Graphs. Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann LeCun, Xavier Bresson |
| 2023 | A Gromov-Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening. Yifan Chen, Rentian Yao, Yun Yang, Jie Chen |
| 2023 | A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining. Shengchao Liu, Weitao Du, Zhi-Ming Ma, Hongyu Guo, Jian Tang |
| 2023 | A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer. Hongyi Pan, Xin Zhu, Salih Furkan Atici, Ahmet Enis Çetin |
| 2023 | A Kernel Stein Test of Goodness of Fit for Sequential Models. Jerome Baum, Heishiro Kanagawa, Arthur Gretton |
| 2023 | A Kernel-Based View of Language Model Fine-Tuning. Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, Sanjeev Arora |
| 2023 | A Kernelized Stein Discrepancy for Biological Sequences. Alan Nawzad Amin, Eli N. Weinstein, Debora Susan Marks |
| 2023 | A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. Victor Dheur, Souhaib Ben Taieb |
| 2023 | A Law of Robustness beyond Isoperimetry. Yihan Wu, Heng Huang, Hongyang Zhang |
| 2023 | A Mathematical Model for Curriculum Learning for Parities. Elisabetta Cornacchia, Elchanan Mossel |
| 2023 | A Model-Based Method for Minimizing CVaR and Beyond. Si Yi Meng, Robert M. Gower |
| 2023 | A Model-free Closeness-of-influence Test for Features in Supervised Learning. Mohammad Mehrabi, Ryan A. Rossi |
| 2023 | A Modern Look at the Relationship between Sharpness and Generalization. Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion |
| 2023 | A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints. Ming Shi, Yingbin Liang, Ness B. Shroff |
| 2023 | A Nearly-Optimal Bound for Fast Regression with ℓ Zhao Song, Mingquan Ye, Junze Yin, Lichen Zhang |
| 2023 | A Neural PDE Solver with Temporal Stencil Modeling. Zhiqing Sun, Yiming Yang, Shinjae Yoo |
| 2023 | A New PHO-rmula for Improved Performance of Semi-Structured Networks. David Rügamer |
| 2023 | A Picture of the Space of Typical Learnable Tasks. Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari |
| 2023 | A Reinforcement Learning Framework for Dynamic Mediation Analysis. Lin Ge, Jitao Wang, Chengchun Shi, Zhenke Wu, Rui Song |
| 2023 | A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks. David Boetius, Stefan Leue, Tobias Sutter |
| 2023 | A Robust Test for the Stationarity Assumption in Sequential Decision Making. Jitao Wang, Chengchun Shi, Zhenke Wu |
| 2023 | A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP. Chi Bach Pham, Wynita M. Griggs, James Saunderson |
| 2023 | A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan |
| 2023 | A Statistical Perspective on Retrieval-Based Models. Soumya Basu, Ankit Singh Rawat, Manzil Zaheer |
| 2023 | A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs. Mikael Henaff, Minqi Jiang, Roberta Raileanu |
| 2023 | A Study on Transformer Configuration and Training Objective. Fuzhao Xue, Jianghai Chen, Aixin Sun, Xiaozhe Ren, Zangwei Zheng, Xiaoxin He, Yongming Chen, Xin Jiang, Yang You |
| 2023 | A Theoretical Analysis of the Learning Dynamics under Class Imbalance. Emanuele Francazi, Marco Baity-Jesi, Aurélien Lucchi |
| 2023 | A Three-regime Model of Network Pruning. Yefan Zhou, Yaoqing Yang, Arin Chang, Michael W. Mahoney |
| 2023 | A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations. Bilal Chughtai, Lawrence Chan, Neel Nanda |
| 2023 | A Two-Stage Active Learning Algorithm for k-Nearest Neighbors. Nicholas Rittler, Kamalika Chaudhuri |
| 2023 | A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition. Shentong Mo, Pedro Morgado |
| 2023 | A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates. Haiyan Jiang, Srinivas Anumasa, Giulia De Masi, Huan Xiong, Bin Gu |
| 2023 | A Unifying Framework to the Analysis of Interaction Methods using Synergy Functions. Daniel Lundström, Meisam Razaviyayn |
| 2023 | A Universal Unbiased Method for Classification from Aggregate Observations. Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen |
| 2023 | A Watermark for Large Language Models. John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein |
| 2023 | A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree. Yury Elkin, Vitaliy Kurlin |
| 2023 | A theory of continuous generative flow networks. Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin |
| 2023 | A theory of representation learning gives a deep generalisation of kernel methods. Adam X. Yang, Maxime Robeyns, Edward Milsom, Ben Anson, Nandi Schoots, Laurence Aitchison |
| 2023 | A/B Testing in Network Data with Covariate-Adaptive Randomization. Jialu Wang, Ping Li, Feifang Hu |
| 2023 | ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging. Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna M. Wardlaw, Grant Mair, Emanuele Trucco, Amos J. Storkey |
| 2023 | AbODE: Ab initio antibody design using conjoined ODEs. Yogesh Verma, Markus Heinonen, Vikas Garg |
| 2023 | Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization. Stone Tao, Xiaochen Li, Tongzhou Mu, Zhiao Huang, Yuzhe Qin, Hao Su |
| 2023 | Abstracting Imperfect Information Away from Two-Player Zero-Sum Games. Samuel Sokota, Ryan D'Orazio, Chun Kai Ling, David J. Wu, J. Zico Kolter, Noam Brown |
| 2023 | Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization. Cheuk Yin Lin, Chaobing Song, Jelena Diakonikolas |
| 2023 | Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations. Jisun Park, Ernest K. Ryu |
| 2023 | Accelerated Primal-Dual Methods for Convex-Strongly-Concave Saddle Point Problems. Mohammad Khalafi, Digvijay Boob |
| 2023 | Accelerated Stochastic Optimization Methods under Quasar-convexity. Qiang Fu, Dongchu Xu, Ashia Camage Wilson |
| 2023 | Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar |
| 2023 | Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations. Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou |
| 2023 | Achieving Hierarchy-Free Approximation for Bilevel Programs with Equilibrium Constraints. Jiayang Li, Jing Yu, Boyi Liu, Yu Marco Nie, Zhaoran Wang |
| 2023 | Achieving High Accuracy with PINNs via Energy Natural Gradient Descent. Johannes Müller, Marius Zeinhofer |
| 2023 | Achieving Linear Speedup in Non-IID Federated Bilevel Learning. Minhui Huang, Dewei Zhang, Kaiyi Ji |
| 2023 | Action Matching: Learning Stochastic Dynamics from Samples. Kirill Neklyudov, Rob Brekelmans, Daniel Severo, Alireza Makhzani |
| 2023 | Active Learning based Structural Inference. Aoran Wang, Jun Pang |
| 2023 | Active Policy Improvement from Multiple Black-box Oracles. Xuefeng Liu, Takuma Yoneda, Chaoqi Wang, Matthew R. Walter, Yuxin Chen |
| 2023 | Active Ranking of Experts Based on their Performances in Many Tasks. El Mehdi Saad, Nicolas Verzelen, Alexandra Carpentier |
| 2023 | Active causal structure learning with advice. Davin Choo, Themistoklis Gouleakis, Arnab Bhattacharyya |
| 2023 | Actor-Critic Alignment for Offline-to-Online Reinforcement Learning. Zishun Yu, Xinhua Zhang |
| 2023 | AdaBoost is not an Optimal Weak to Strong Learner. Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert |
| 2023 | AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation. Yifan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan |
| 2023 | AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners. Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, Ping Luo |
| 2023 | Adapting to game trees in zero-sum imperfect information games. Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko |
| 2023 | Adaptive Annealed Importance Sampling with Constant Rate Progress. Shirin Goshtasbpour, Victor Cohen, Fernando Pérez-Cruz |
| 2023 | Adaptive Barrier Smoothing for First-Order Policy Gradient with Contact Dynamics. Shenao Zhang, Wanxin Jin, Zhaoran Wang |
| 2023 | Adaptive Compositional Continual Meta-Learning. Bin Wu, Jinyuan Fang, Xiangxiang Zeng, Shangsong Liang, Qiang Zhang |
| 2023 | Adaptive Computation with Elastic Input Sequence. Fuzhao Xue, Valerii Likhosherstov, Anurag Arnab, Neil Houlsby, Mostafa Dehghani, Yang You |
| 2023 | Adaptive Coordination in Social Embodied Rearrangement. Andrew Szot, Unnat Jain, Dhruv Batra, Zsolt Kira, Ruta Desai, Akshara Rai |
| 2023 | Adaptive Estimation of Graphical Models under Total Positivity. Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar |
| 2023 | Adaptive IMLE for Few-shot Pretraining-free Generative Modelling. Mehran Aghabozorgi, Shichong Peng, Ke Li |
| 2023 | Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. Alicia Curth, Alihan Hüyük, Mihaela van der Schaar |
| 2023 | Adaptive Smoothing Gradient Learning for Spiking Neural Networks. Ziming Wang, Runhao Jiang, Shuang Lian, Rui Yan, Huajin Tang |
| 2023 | Adaptive Whitening in Neural Populations with Gain-modulating Interneurons. Lyndon R. Duong, David Lipshutz, David J. Heeger, Dmitri B. Chklovskii, Eero P. Simoncelli |
| 2023 | Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift. Yijun Dong, Yuege Xie, Rachel A. Ward |
| 2023 | Additive Causal Bandits with Unknown Graph. Alan Malek, Virginia Aglietti, Silvia Chiappa |
| 2023 | Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. Boxin Zhao, Boxiang Lyu, Raul Castro Fernandez, Mladen Kolar |
| 2023 | Adversarial Cheap Talk. Chris Lu, Timon Willi, Alistair Letcher, Jakob Nicolaus Foerster |
| 2023 | Adversarial Collaborative Learning on Non-IID Features. Qinbin Li, Bingsheng He, Dawn Song |
| 2023 | Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan |
| 2023 | Adversarial Learning of Distributional Reinforcement Learning. Yang Sui, Yukun Huang, Hongtu Zhu, Fan Zhou |
| 2023 | Adversarial Parameter Attack on Deep Neural Networks. Lijia Yu, Yihan Wang, Xiao-Shan Gao |
| 2023 | Adversarial Policies Beat Superhuman Go AIs. Tony Tong Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor Pogrebniak, Sergey Levine, Stuart Russell |
| 2023 | Adversarial robustness of amortized Bayesian inference. Manuel Glöckler, Michael Deistler, Jakob H. Macke |
| 2023 | Adversarially Robust PAC Learnability of Real-Valued Functions. Idan Attias, Steve Hanneke |
| 2023 | Algorithmic Collective Action in Machine Learning. Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, Tijana Zrnic |
| 2023 | Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions. Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban, Umut Simsekli |
| 2023 | Algorithms for bounding contribution for histogram estimation under user-level privacy. Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu, Peter Kairouz, Marco Gruteser |
| 2023 | Aligning Language Models with Preferences through f-divergence Minimization. Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Nahyeon Ryu, Marc Dymetman |
| 2023 | All in a Row: Compressed Convolution Networks for Graphs. Junshu Sun, Shuhui Wang, Xinzhe Han, Zhe Xue, Qingming Huang |
| 2023 | Alternately Optimized Graph Neural Networks. Haoyu Han, Xiaorui Liu, Haitao Mao, MohamadAli Torkamani, Feng Shi, Victor Lee, Jiliang Tang |
| 2023 | Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations. Chao Li, Junhua Zeng, Chunmei Li, Cesar F. Caiafa, Qibin Zhao |
| 2023 | An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning. Woojun Kim, Youngchul Sung |
| 2023 | An Effective Meaningful Way to Evaluate Survival Models. Shiang Qi, Neeraj Kumar, Mahtab Farrokh, Weijie Sun, Li-Hao Kuan, Rajesh Ranganath, Ricardo Henao, Russell Greiner |
| 2023 | An Information-Theoretic Analysis of Nonstationary Bandit Learning. Seungki Min, Daniel Russo |
| 2023 | An Instrumental Variable Approach to Confounded Off-Policy Evaluation. Yang Xu, Jin Zhu, Chengchun Shi, Shikai Luo, Rui Song |
| 2023 | An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning. Jaesik Yoon, Yi-Fu Wu, Heechul Bae, Sungjin Ahn |
| 2023 | An SDE for Modeling SAM: Theory and Insights. Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi |
| 2023 | Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation. Xiaoyun Li, Ping Li |
| 2023 | Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels. Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu |
| 2023 | Analyzing Diffusion as Serial Reproduction. Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths |
| 2023 | Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano. Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi |
| 2023 | Anchor Sampling for Federated Learning with Partial Client Participation. Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao |
| 2023 | Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization. Yushi Bai, Xin Lv, Juanzi Li, Lei Hou |
| 2023 | Anti-Exploration by Random Network Distillation. Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov, Sergey Kolesnikov |
| 2023 | Applied Online Algorithms with Heterogeneous Predictors. Jessica Maghakian, Russell Lee, Mohammad Hajiesmaili, Jian Li, Ramesh K. Sitaraman, Zhenhua Liu |
| 2023 | Approximate Causal Effect Identification under Weak Confounding. Ziwei Jiang, Lai Wei, Murat Kocaoglu |
| 2023 | Approximate Stein Classes for Truncated Density Estimation. Daniel J. Williams, Song Liu |
| 2023 | Approximately Optimal Core Shapes for Tensor Decompositions. Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni |
| 2023 | Approximation Algorithms for Fair Range Clustering. Sèdjro Salomon Hotegni, Sepideh Mahabadi, Ali Vakilian |
| 2023 | Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input. Shokichi Takakura, Taiji Suzuki |
| 2023 | Architecture-Agnostic Masked Image Modeling - From ViT back to CNN. Siyuan Li, Di Wu, Fang Wu, Zelin Zang, Stan Z. Li |
| 2023 | Are Diffusion Models Vulnerable to Membership Inference Attacks? Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu |
| 2023 | Are Equivariant Equilibrium Approximators Beneficial? Zhijian Duan, Yunxuan Ma, Xiaotie Deng |
| 2023 | Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation. Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan |
| 2023 | Are Large Kernels Better Teachers than Transformers for ConvNets? Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu |
| 2023 | Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations. Yongyi Yang, Jacob Steinhardt, Wei Hu |
| 2023 | Are Random Decompositions all we need in High Dimensional Bayesian Optimisation? Juliusz Krysztof Ziomek, Haitham Bou-Ammar |
| 2023 | Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. Aude Sportisse, Hugo Schmutz, Olivier Humbert, Charles Bouveyron, Pierre-Alexandre Mattei |
| 2023 | Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models. Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Tachard Passos, Sumit Sanghai |
| 2023 | Atari-5: Distilling the Arcade Learning Environment down to Five Games. Matthew Aitchison, Penny Sweetser, Marcus Hutter |
| 2023 | Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability. Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein, Michael Kölle, Thomas Gabor, Claudia Linnhoff-Popien |
| 2023 | Attribute-Efficient PAC Learning of Low-Degree Polynomial Threshold Functions with Nasty Noise. Shiwei Zeng, Jie Shen |
| 2023 | Attributing Image Generative Models using Latent Fingerprints. Guangyu Nie, Changhoon Kim, Yezhou Yang, Yi Ren |
| 2023 | AudioLDM: Text-to-Audio Generation with Latent Diffusion Models. Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo P. Mandic, Wenwu Wang, Mark D. Plumbley |
| 2023 | Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning. Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine |
| 2023 | AutoCoreset: An Automatic Practical Coreset Construction Framework. Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus |
| 2023 | Automated Search for Conjectures on Mathematical Constants using Analysis of Integer Sequences. Ofir Razon, Yoav Harris, Shahar Gottlieb, Dan Carmon, Ofir David, Ido Kaminer |
| 2023 | Automatic Data Augmentation via Invariance-Constrained Learning. Ignacio Hounie, Luiz F. O. Chamon, Alejandro Ribeiro |
| 2023 | Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning. Mingqi Yuan, Bo Li, Xin Jin, Wenjun Zeng |
| 2023 | Automatically Auditing Large Language Models via Discrete Optimization. Erik Jones, Anca D. Dragan, Aditi Raghunathan, Jacob Steinhardt |
| 2023 | Automatically marginalized MCMC in probabilistic programming. Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon |
| 2023 | Autoregressive Diffusion Model for Graph Generation. Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang |
| 2023 | Auxiliary Learning as an Asymmetric Bargaining Game. Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, Ethan Fetaya |
| 2023 | Auxiliary Modality Learning with Generalized Curriculum Distillation. Yu Shen, Xijun Wang, Peng Gao, Ming C. Lin |
| 2023 | Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity. Risheng Liu, Yaohua Liu, Wei Yao, Shangzhi Zeng, Jin Zhang |
| 2023 | B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit |
| 2023 | BEATs: Audio Pre-Training with Acoustic Tokenizers. Sanyuan Chen, Yu Wu, Chengyi Wang, Shujie Liu, Daniel Tompkins, Zhuo Chen, Wanxiang Che, Xiangzhan Yu, Furu Wei |
| 2023 | BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. Junnan Li, Dongxu Li, Silvio Savarese, Steven C. H. Hoi |
| 2023 | BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming. Steven Adams, Andrea Patane, Morteza Lahijanian, Luca Laurenti |
| 2023 | BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models. Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, Byung-Gon Chun |
| 2023 | Bag of Tricks for Training Data Extraction from Language Models. Weichen Yu, Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, Shuicheng Yan |
| 2023 | Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits. Zongqi Wan, Jialin Zhang, Wei Chen, Xiaoming Sun, Zhijie Zhang |
| 2023 | Bandit Online Linear Optimization with Hints and Queries. Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit |
| 2023 | Bandits with Knapsacks: Advice on Time-Varying Demands. Lixing Lyu, Wang Chi Cheung |
| 2023 | Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning. Jiatai Huang, Yan Dai, Longbo Huang |
| 2023 | Bayes-optimal Learning of Deep Random Networks of Extensive-width. Hugo Cui, Florent Krzakala, Lenka Zdeborová |
| 2023 | Bayesian Design Principles for Frequentist Sequential Learning. Yunbei Xu, Assaf Zeevi |
| 2023 | Bayesian Estimation of Differential Privacy. Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Ahmed Salem, Victor Rühle, Andrew Paverd, Mohammad Naseri, Boris Köpf, Daniel Jones |
| 2023 | Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang |
| 2023 | Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process. Zhibin Duan, Xinyang Liu, Yudi Su, Yishi Xu, Bo Chen, Mingyuan Zhou |
| 2023 | Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models. Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone |
| 2023 | Bayesian online change point detection with Hilbert space approximate Student-t process. Jeremy Sellier, Petros Dellaportas |
| 2023 | Beam Tree Recursive Cells. Jishnu Ray Chowdhury, Cornelia Caragea |
| 2023 | Behavior Contrastive Learning for Unsupervised Skill Discovery. Rushuai Yang, Chenjia Bai, Hongyi Guo, Siyuan Li, Bin Zhao, Zhen Wang, Peng Liu, Xuelong Li |
| 2023 | Benign Overfitting in Deep Neural Networks under Lazy Training. Zhenyu Zhu, Fanghui Liu, Grigorios Chrysos, Francesco Locatello, Volkan Cevher |
| 2023 | Benign Overfitting in Two-layer ReLU Convolutional Neural Networks. Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu |
| 2023 | Best Arm Identification in Multi-Agent Multi-Armed Bandits. Filippo Vannella, Alexandre Proutière, Jaeseong Jeong |
| 2023 | Best of Both Worlds Policy Optimization. Christoph Dann, Chen-Yu Wei, Julian Zimmert |
| 2023 | Better Diffusion Models Further Improve Adversarial Training. Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, Shuicheng Yan |
| 2023 | Better Training of GFlowNets with Local Credit and Incomplete Trajectories. Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio |
| 2023 | Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic. Wesley A. Suttle, Amrit S. Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel, Dinesh Manocha |
| 2023 | Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering. Erlin Pan, Zhao Kang |
| 2023 | Beyond In-Domain Scenarios: Robust Density-Aware Calibration. Christian Tomani, Futa Kai Waseda, Yuesong Shen, Daniel Cremers |
| 2023 | Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization. Zhengmian Hu, Xidong Wu, Heng Huang |
| 2023 | Beyond Reward: Offline Preference-guided Policy Optimization. Yachen Kang, Diyuan Shi, Jinxin Liu, Li He, Donglin Wang |
| 2023 | Beyond Uniform Lipschitz Condition in Differentially Private Optimization. Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi |
| 2023 | Beyond the Edge of Stability via Two-step Gradient Updates. Lei Chen, Joan Bruna |
| 2023 | Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels. Simone Bombari, Shayan Kiyani, Marco Mondelli |
| 2023 | Bi-directional Masks for Efficient N: M Sparse Training. Yuxin Zhang, Yiting Luo, Mingbao Lin, Yunshan Zhong, Jingjing Xie, Fei Chao, Rongrong Ji |
| 2023 | BiBench: Benchmarking and Analyzing Network Binarization. Haotong Qin, Mingyuan Zhang, Yifu Ding, Aoyu Li, Zhongang Cai, Ziwei Liu, Fisher Yu, Xianglong Liu |
| 2023 | BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning. Kishaan Jeeveswaran, Prashant Shivaram Bhat, Bahram Zonooz, Elahe Arani |
| 2023 | Biases in Evaluation of Molecular Optimization Methods and Bias Reduction Strategies. Hiroshi Kajino, Kohei Miyaguchi, Takayuki Osogami |
| 2023 | Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions. Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Yuke Xiang, Yufeng Li |
| 2023 | Bidirectional Learning for Offline Model-based Biological Sequence Design. Can Chen, Yingxue Zhang, Xue Liu, Mark Coates |
| 2023 | Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers. Yineng Chen, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao |
| 2023 | Bigger, Better, Faster: Human-level Atari with human-level efficiency. Max Schwarzer, Johan S. Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro |
| 2023 | Bilevel Optimization with Coupled Decision-Dependent Distributions. Songtao Lu |
| 2023 | Bit Allocation using Optimization. Tongda Xu, Han Gao, Chenjian Gao, Yuanyuan Wang, Dailan He, Jinyong Pi, Jixiang Luo, Ziyu Zhu, Mao Ye, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang |
| 2023 | Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces. Javier E. Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin |
| 2023 | Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures. Oleg Balabanov, Matthias Beaupère, Laura Grigori, Victor Lederer |
| 2023 | Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang |
| 2023 | Blossom: an Anytime Algorithm for Computing Optimal Decision Trees. Emir Demirovic, Emmanuel Hebrard, Louis Jean |
| 2023 | Boosting Graph Contrastive Learning via Graph Contrastive Saliency. Chunyu Wei, Yu Wang, Bing Bai, Kai Ni, David Brady, Lu Fang |
| 2023 | Boosting Offline Reinforcement Learning with Action Preference Query. Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin, Shiji Song, Gao Huang |
| 2023 | Bootstrap in High Dimension with Low Computation. Henry Lam, Zhenyuan Liu |
| 2023 | Bootstrapped Representations in Reinforcement Learning. Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney |
| 2023 | Brainformers: Trading Simplicity for Efficiency. Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng, Chang Lan, Da Huang, Siamak Shakeri, David R. So, Andrew M. Dai, Yifeng Lu, Zhifeng Chen, Quoc V. Le, Claire Cui, James Laudon, Jeff Dean |
| 2023 | Brauer's Group Equivariant Neural Networks. Edward Pearce-Crump |
| 2023 | Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach. Xuan Son Nguyen, Shuo Yang |
| 2023 | Buying Information for Stochastic Optimization. Mingchen Ma, Christos Tzamos |
| 2023 | Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting. Yuchen Liu, Chen Chen, Lingjuan Lyu, Fangzhao Wu, Sai Wu, Gang Chen |
| 2023 | CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling. Jun Zhang, Shuyang Jiang, Jiangtao Feng, Lin Zheng, Lingpeng Kong |
| 2023 | CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets. Zachary Novack, Julian J. McAuley, Zachary Chase Lipton, Saurabh Garg |
| 2023 | CLIPood: Generalizing CLIP to Out-of-Distributions. Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long |
| 2023 | CLUSTSEG: Clustering for Universal Segmentation. James Chenhao Liang, Tianfei Zhou, Dongfang Liu, Wenguan Wang |
| 2023 | CLUTR: Curriculum Learning via Unsupervised Task Representation Learning. Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis, Aleksandra Faust, Pieter Abbeel, Ion Stoica |
| 2023 | CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster |
| 2023 | COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects. Anlan Yu, Ning Lyu, Jieming Yin, Zhiyuan Yan, Wujie Wen |
| 2023 | COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models. Jinqi Xiao, Miao Yin, Yu Gong, Xiao Zang, Jian Ren, Bo Yuan |
| 2023 | CRISP: Curriculum based Sequential neural decoders for Polar code family. S. Ashwin Hebbar, Viraj Vivek Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh, Pramod Viswanath |
| 2023 | CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations. Gengchen Mai, Ni Lao, Yutong He, Jiaming Song, Stefano Ermon |
| 2023 | Calibrating Multimodal Learning. Huan Ma, Qingyang Zhang, Changqing Zhang, Bingzhe Wu, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu |
| 2023 | Can Forward Gradient Match Backpropagation? Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon |
| 2023 | Can Large Language Models Reason about Program Invariants? Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin |
| 2023 | Can Neural Network Memorization Be Localized? Pratyush Maini, Michael Curtis Mozer, Hanie Sedghi, Zachary Chase Lipton, J. Zico Kolter, Chiyuan Zhang |
| 2023 | Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? Boris Knyazev, Doha Hwang, Simon Lacoste-Julien |
| 2023 | CataBEEM: Integrating Latent Interaction Categories in Node-wise Community Detection Models for Network Data. Yuhua Zhang, Walter H. Dempsey |
| 2023 | Causal Bounds in Quasi-Markovian Graphs. Madhumitha Shridharan, Garud Iyengar |
| 2023 | Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang |
| 2023 | Causal Isotonic Calibration for Heterogeneous Treatment Effects. Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone, Alex Luedtke |
| 2023 | Causal Modeling of Policy Interventions From Treatment-Outcome Sequences. Caglar Hizli, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietiläinen, Pekka Marttinen |
| 2023 | Causal Proxy Models for Concept-based Model Explanations. Zhengxuan Wu, Karel D'Oosterlinck, Atticus Geiger, Amir Zur, Christopher Potts |
| 2023 | Causal Strategic Classification: A Tale of Two Shifts. Guy Horowitz, Nir Rosenfeld |
| 2023 | Causal Structure Learning for Latent Intervened Non-stationary Data. Chenxi Liu, Kun Kuang |
| 2023 | Cell-Free Latent Go-Explore. Quentin Gallouédec, Emmanuel Dellandréa |
| 2023 | Certified Robust Neural Networks: Generalization and Corruption Resistance. M. Amine Bennouna, Ryan Lucas, Bart P. G. Van Parys |
| 2023 | Certifying Ensembles: A General Certification Theory with S-Lipschitzness. Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip H. S. Torr, Adel Bibi |
| 2023 | Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning. Yanbo Dai, Songze Li |
| 2023 | Change is Hard: A Closer Look at Subpopulation Shift. Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi |
| 2023 | Chemically Transferable Generative Backmapping of Coarse-Grained Proteins. Soojung Yang, Rafael Gómez-Bombarelli |
| 2023 | ChiPFormer: Transferable Chip Placement via Offline Decision Transformer. Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, Ping Luo |
| 2023 | CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling. Yansen Wang, Xinyang Jiang, Kan Ren, Caihua Shan, Xufang Luo, Dongqi Han, Kaitao Song, Yifei Shen, Dongsheng Li |
| 2023 | ClimaX: A foundation model for weather and climate. Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover |
| 2023 | Cluster Explanation via Polyhedral Descriptions. Connor Lawless, Oktay Günlük |
| 2023 | ClusterFuG: Clustering Fully connected Graphs by Multicut. Ahmed Abbas, Paul Swoboda |
| 2023 | CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification. Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, Xiao Luo |
| 2023 | CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis. Chaejeong Lee, Jayoung Kim, Noseong Park |
| 2023 | Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D. Bo Qiang, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao, Hao Zhou, Wei-Ying Ma, Yanyan Lan |
| 2023 | Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, Hsien-Hsin S. Lee |
| 2023 | CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, Ce Zhang |
| 2023 | CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models. Zhiyuan Yu, Yuhao Wu, Ning Zhang, Chenguang Wang, Yevgeniy Vorobeychik, Chaowei Xiao |
| 2023 | Coder Reviewer Reranking for Code Generation. Tianyi Zhang, Tao Yu, Tatsunori Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, Sida Wang |
| 2023 | Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates. Louis Sharrock, Christopher Nemeth |
| 2023 | Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator. Alexander Shekhovtsov |
| 2023 | Collaborative Causal Inference with Fair Incentives. Rui Qiao, Xinyi Xu, Bryan Kian Hsiang Low |
| 2023 | Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits. Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant |
| 2023 | Combinatorial Neural Bandits. Taehyun Hwang, Kyuwook Chai, Min-hwan Oh |
| 2023 | Communication-Constrained Bandits under Additive Gaussian Noise. Prathamesh Mayekar, Jonathan Scarlett, Vincent Y. F. Tan |
| 2023 | Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation. Peiyao Xiao, Kaiyi Ji |
| 2023 | Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects. Naoufal Acharki, Ramiro Lugo, Antoine Bertoncello, Josselin Garnier |
| 2023 | Competing for Shareable Arms in Multi-Player Multi-Armed Bandits. Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui |
| 2023 | Competitive Gradient Optimization. Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli |
| 2023 | Complementary Attention for Multi-Agent Reinforcement Learning. Jianzhun Shao, Hongchang Zhang, Yun Qu, Chang Liu, Shuncheng He, Yuhang Jiang, Xiangyang Ji |
| 2023 | Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning. Dohyun Kwon, Hanbaek Lyu |
| 2023 | Composer: Creative and Controllable Image Synthesis with Composable Conditions. Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, Jingren Zhou |
| 2023 | Compositional Exemplars for In-context Learning. Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, Lingpeng Kong |
| 2023 | Compositional Score Modeling for Simulation-Based Inference. Tomas Geffner, George Papamakarios, Andriy Mnih |
| 2023 | Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data. Yonggui Yan, Jie Chen, Pin-Yu Chen, Xiaodong Cui, Songtao Lu, Yangyang Xu |
| 2023 | Compressing Tabular Data via Latent Variable Estimation. Andrea Montanari, Eric Weiner |
| 2023 | Computational Asymmetries in Robust Classification. Samuele Marro, Michele Lombardi |
| 2023 | Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions. Nicolas Chopin, Andras Fulop, Jeremy Heng, Alexandre H. Thiery |
| 2023 | Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun |
| 2023 | ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction. Wang Zhang, Tsui-Wei Weng, Subhro Das, Alexandre Megretski, Luca Daniel, Lam M. Nguyen |
| 2023 | Concept-based Explanations for Out-of-Distribution Detectors. Jihye Choi, Jayaram Raghuram, Ryan Feng, Jiefeng Chen, Somesh Jha, Atul Prakash |
| 2023 | Concurrent Shuffle Differential Privacy Under Continual Observation. Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer |
| 2023 | Conditional Graph Information Bottleneck for Molecular Relational Learning. Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim, Junseok Lee, Chanyoung Park |
| 2023 | Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models. Harshit Varma, Abhijeet Awasthi, Sunita Sarawagi |
| 2023 | Conditionally Strongly Log-Concave Generative Models. Florentin Guth, Etienne Lempereur, Joan Bruna, Stéphane Mallat |
| 2023 | Cones: Concept Neurons in Diffusion Models for Customized Generation. Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, Yang Cao |
| 2023 | Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation. Weijian Deng, Yumin Suh, Stephen Gould, Liang Zheng |
| 2023 | Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping. Ziyi Liang, Yanfei Zhou, Matteo Sesia |
| 2023 | Conformal Prediction Sets for Graph Neural Networks. Soroush H. Zargarbashi, Simone Antonelli, Aleksandar Bojchevski |
| 2023 | Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov |
| 2023 | Conformal Prediction with Missing Values. Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano |
| 2023 | Conformalization of Sparse Generalized Linear Models. Etash Kumar Guha, Eugène Ndiaye, Xiaoming Huo |
| 2023 | Consistency Models. Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever |
| 2023 | Consistency of Multiple Kernel Clustering. Weixuan Liang, Xinwang Liu, Yong Liu, Chuan Ma, Yunping Zhao, Zhe Liu, En Zhu |
| 2023 | Constant Matters: Fine-grained Error Bound on Differentially Private Continual Observation. Hendrik Fichtenberger, Monika Henzinger, Jalaj Upadhyay |
| 2023 | Constrained Causal Bayesian Optimization. Virginia Aglietti, Alan Malek, Ira Ktena, Silvia Chiappa |
| 2023 | Constrained Decision Transformer for Offline Safe Reinforcement Learning. Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao |
| 2023 | Constrained Efficient Global Optimization of Expensive Black-box Functions. Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones |
| 2023 | Constrained Monotonic Neural Networks. Davor Runje, Sharath M. Shankaranarayana |
| 2023 | Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching. Ilgee Hong, Sen Na, Michael W. Mahoney, Mladen Kolar |
| 2023 | Constrained Phi-Equilibria. Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Francesco Trovò, Nicola Gatti |
| 2023 | Context Consistency Regularization for Label Sparsity in Time Series. Yooju Shin, Susik Yoon, Hwanjun Song, Dongmin Park, Byunghyun Kim, Jae-Gil Lee, Byung Suk Lee |
| 2023 | Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning. Dingyang Chen, Qi Zhang |
| 2023 | Contextual Combinatorial Bandits with Probabilistically Triggered Arms. Xutong Liu, Jinhang Zuo, Siwei Wang, John C. S. Lui, Mohammad Hajiesmaili, Adam Wierman, Wei Chen |
| 2023 | Contextual Conservative Interleaving Bandits. Kei Takemura |
| 2023 | Contextual Reliability: When Different Features Matter in Different Contexts. Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan |
| 2023 | Continual Learners are Incremental Model Generalizers. Jaehong Yoon, Sung Ju Hwang, Yue Cao |
| 2023 | Continual Learning in Linear Classification on Separable Data. Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry |
| 2023 | Continual Task Allocation in Meta-Policy Network via Sparse Prompting. Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, Yuhui Shi |
| 2023 | Continual Vision-Language Representation Learning with Off-Diagonal Information. Zixuan Ni, Longhui Wei, Siliang Tang, Yueting Zhuang, Qi Tian |
| 2023 | Continuation Path Learning for Homotopy Optimization. Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang |
| 2023 | Continuous Spatiotemporal Transformer. Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David van Dijk |
| 2023 | Continuously Parameterized Mixture Models. Christopher M. Bender, Yifeng Shi, Marc Niethammer, Junier Oliva |
| 2023 | ContraBAR: Contrastive Bayes-Adaptive Deep RL. Era Choshen, Aviv Tamar |
| 2023 | Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining. Zekun Qi, Runpei Dong, Guofan Fan, Zheng Ge, Xiangyu Zhang, Kaisheng Ma, Li Yi |
| 2023 | Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning. Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu |
| 2023 | Contrastive Learning Meets Homophily: Two Birds with One Stone. Dongxiao He, Jitao Zhao, Rui Guo, Zhiyong Feng, Di Jin, Yuxiao Huang, Zhen Wang, Weixiong Zhang |
| 2023 | Controllability-Aware Unsupervised Skill Discovery. Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel |
| 2023 | Controllable Neural Symbolic Regression. Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny |
| 2023 | Controlled Differential Equations on Long Sequences via Non-standard Wavelets. Sourav Pal, Zhanpeng Zeng, Sathya N. Ravi, Vikas Singh |
| 2023 | Controlled Text Generation with Natural Language Instructions. Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox, Ryan Cotterell, Mrinmaya Sachan |
| 2023 | Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network. Yuri Kinoshita, Kenta Oono, Kenji Fukumizu, Yuichi Yoshida, Shin-ichi Maeda |
| 2023 | Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification. Dong Xing, Pengjie Gu, Qian Zheng, Xinrun Wang, Shanqi Liu, Longtao Zheng, Bo An, Gang Pan |
| 2023 | Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data. Ahmet Alacaoglu, Hanbaek Lyu |
| 2023 | Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity. Eduard Gorbunov, Adrien B. Taylor, Samuel Horváth, Gauthier Gidel |
| 2023 | Convex Geometry of ReLU-layers, Injectivity on the Ball and Local Reconstruction. Daniel Haider, Martin Ehler, Péter Balázs |
| 2023 | Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders. Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt, Víctor Elvira, Jens Lagergren |
| 2023 | Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu |
| 2023 | Cooperative Open-ended Learning Framework for Zero-Shot Coordination. Yang Li, Shao Zhang, Jichen Sun, Yali Du, Ying Wen, Xinbing Wang, Wei Pan |
| 2023 | Coordinate Descent Methods for Fractional Minimization. Ganzhao Yuan |
| 2023 | Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets. Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen, Xiang Yan, Xiaotie Deng |
| 2023 | Correcting discount-factor mismatch in on-policy policy gradient methods. Fengdi Che, Gautham Vasan, A. Rupam Mahmood |
| 2023 | Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang |
| 2023 | Counterfactual Analysis in Dynamic Latent State Models. Martin B. Haugh, Raghav Singal |
| 2023 | Counterfactual Identifiability of Bijective Causal Models. Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah |
| 2023 | Coupled Variational Autoencoder. Xiaoran Hao, Patrick Shafto |
| 2023 | Covariate balancing using the integral probability metric for causal inference. Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim |
| 2023 | Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution. Ruofan Zhang, Jinjin Gu, Haoyu Chen, Chao Dong, Yulun Zhang, Wenming Yang |
| 2023 | Cramming: Training a Language Model on a single GPU in one day. Jonas Geiping, Tom Goldstein |
| 2023 | Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss. Pierre Bréchet, Katerina Papagiannouli, Jing An, Guido Montúfar |
| 2023 | Cross-Entropy Loss Functions: Theoretical Analysis and Applications. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2023 | Cross-Modal Fine-Tuning: Align then Refine. Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar |
| 2023 | CrossSplit: Mitigating Label Noise Memorization through Data Splitting. Jihye Kim, Aristide Baratin, Yan Zhang, Simon Lacoste-Julien |
| 2023 | Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard, Rémi Munos, Michal Valko |
| 2023 | Curious Replay for Model-based Adaptation. Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber |
| 2023 | Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation. Xin Wang, Zirui Pan, Yuwei Zhou, Hong Chen, Chendi Ge, Wenwu Zhu |
| 2023 | Cut your Losses with Squentropy. Like Hui, Mikhail Belkin, Stephen Wright |
| 2023 | Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization. Xufeng Cai, Chaobing Song, Stephen J. Wright, Jelena Diakonikolas |
| 2023 | D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching. Xuanzhou Liu, Lin Zhang, Jiaqi Sun, Yujiu Yang, Haiqin Yang |
| 2023 | DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization. Adel Nabli, Edouard Oyallon |
| 2023 | DDGR: Continual Learning with Deep Diffusion-based Generative Replay. Rui Gao, Weiwei Liu |
| 2023 | DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning. Tomoya Murata, Taiji Suzuki |
| 2023 | DIVISION: Memory Efficient Training via Dual Activation Precision. Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Ben Hu |
| 2023 | DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference. Wanrong Zhang, Ruqi Zhang |
| 2023 | DRCFS: Doubly Robust Causal Feature Selection. Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Cristian R. Rojas, Stefan Bauer |
| 2023 | DRew: Dynamically Rewired Message Passing with Delay. Benjamin Gutteridge, Xiaowen Dong, Michael M. Bronstein, Francesco Di Giovanni |
| 2023 | DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-tau Yih, Daniel Fried, Sida I. Wang, Tao Yu |
| 2023 | DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm. Lisang Ding, Kexin Jin, Bicheng Ying, Kun Yuan, Wotao Yin |
| 2023 | DUET: 2D Structured and Approximately Equivariant Representations. Xavier Suau, Federico Danieli, T. Anderson Keller, Arno Blaas, Chen Huang, Jason Ramapuram, Dan Busbridge, Luca Zappella |
| 2023 | Data Efficient Neural Scaling Law via Model Reusing. Peihao Wang, Rameswar Panda, Zhangyang Wang |
| 2023 | Data Feedback Loops: Model-driven Amplification of Dataset Biases. Rohan Taori, Tatsunori Hashimoto |
| 2023 | Data Poisoning Attacks Against Multimodal Encoders. Ziqing Yang, Xinlei He, Zheng Li, Michael Backes, Mathias Humbert, Pascal Berrang, Yang Zhang |
| 2023 | Data Representations' Study of Latent Image Manifolds. Ilya Kaufman, Omri Azencot |
| 2023 | Data Structures for Density Estimation. Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Sandeep Silwal |
| 2023 | Data-Copying in Generative Models: A Formal Framework. Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri |
| 2023 | Data-Driven Subgroup Identification for Linear Regression. Zachary Izzo, Ruishan Liu, James Zou |
| 2023 | Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least. Siddharth Joshi, Baharan Mirzasoleiman |
| 2023 | Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value. Yongchan Kwon, James Zou |
| 2023 | Dataset Distillation with Convexified Implicit Gradients. Noel Loo, Ramin M. Hasani, Mathias Lechner, Daniela Rus |
| 2023 | DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models. Liangbin Xie, Xintao Wang, Xiangyu Chen, Gen Li, Ying Shan, Jiantao Zhou, Chao Dong |
| 2023 | Decentralized SGD and Average-direction SAM are Asymptotically Equivalent. Tongtian Zhu, Fengxiang He, Kaixuan Chen, Mingli Song, Dacheng Tao |
| 2023 | Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity. Xuxing Chen, Minhui Huang, Shiqian Ma, Krishna Balasubramanian |
| 2023 | Decoding Layer Saliency in Language Transformers. Elizabeth Mary Hou, Gregory David Castañón |
| 2023 | DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu |
| 2023 | Deep Anomaly Detection under Labeling Budget Constraints. Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph |
| 2023 | Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach. Tri Nguyen, Shahana Ibrahim, Xiao Fu |
| 2023 | Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search. Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier, Marco Virgolin |
| 2023 | Deep Graph Representation Learning and Optimization for Influence Maximization. Chen Ling, Junji Jiang, Junxiang Wang, My T. Thai, Renhao Xue, James Song, Meikang Qiu, Liang Zhao |
| 2023 | Deep Laplacian-based Options for Temporally-Extended Exploration. Martin Klissarov, Marlos C. Machado |
| 2023 | Deep Latent State Space Models for Time-Series Generation. Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon |
| 2023 | Deep Perturbation Learning: Enhancing the Network Performance via Image Perturbations. Zifan Song, Xiao Gong, Guosheng Hu, Cairong Zhao |
| 2023 | Deep Regression Unlearning. Ayush Kumar Tarun, Vikram Singh Chundawat, Murari Mandal, Mohan S. Kankanhalli |
| 2023 | Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery. Dingrong Wang, Deep Shankar Pandey, Krishna Prasad Neupane, Zhiwei Yu, Ervine Zheng, Zhi Zheng, Qi Yu |
| 2023 | Defects of Convolutional Decoder Networks in Frequency Representation. Ling Tang, Wen Shen, Zhanpeng Zhou, Yuefeng Chen, Quanshi Zhang |
| 2023 | Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen |
| 2023 | Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback. Tal Lancewicki, Aviv Rosenberg, Dmitry Sotnikov |
| 2023 | Delay-agnostic Asynchronous Coordinate Update Algorithm. Xuyang Wu, Changxin Liu, Sindri Magnússon, Mikael Johansson |
| 2023 | Delayed Bandits: When Do Intermediate Observations Help? Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Yevgeny Seldin |
| 2023 | Delayed Feedback in Kernel Bandits. Sattar Vakili, Danyal Ahmed, Alberto Bernacchia, Ciara Pike-Burke |
| 2023 | Delving into Noisy Label Detection with Clean Data. Chenglin Yu, Xinsong Ma, Weiwei Liu |
| 2023 | Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum. Jigang Kim, Daesol Cho, H. Jin Kim |
| 2023 | Demystifying Disagreement-on-the-Line in High Dimensions. Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani |
| 2023 | Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks. He Zhang, Bang Wu, Shuo Wang, Xiangwen Yang, Minhui Xue, Shirui Pan, Xingliang Yuan |
| 2023 | Denoising MCMC for Accelerating Diffusion-Based Generative Models. Beomsu Kim, Jong Chul Ye |
| 2023 | DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, Chelsea Finn |
| 2023 | Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score. Shuhai Zhang, Feng Liu, Jiahao Yang, Yifan Yang, Changsheng Li, Bo Han, Mingkui Tan |
| 2023 | Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions. Ezgi Korkmaz, Jonah Brown-Cohen |
| 2023 | Detecting Out-of-distribution Data through In-distribution Class Prior. Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han |
| 2023 | Deterministic equivalent and error universality of deep random features learning. Dominik Schröder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro |
| 2023 | DevFormer: A Symmetric Transformer for Context-Aware Device Placement. Haeyeon Kim, Minsu Kim, Federico Berto, Joungho Kim, Jinkyoo Park |
| 2023 | Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation. Andi Peng, Aviv Netanyahu, Mark K. Ho, Tianmin Shu, Andreea Bobu, Julie Shah, Pulkit Agrawal |
| 2023 | Difference of submodular minimization via DC programming. Marwa El Halabi, George Orfanides, Tim Hoheisel |
| 2023 | Difference-in-Differences Meets Tree-based Methods: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding. Caizhi Tang, Huiyuan Wang, Xinyu Li, Qing Cui, Longfei Li, Jun Zhou |
| 2023 | Differentiable Multi-Target Causal Bayesian Experimental Design. Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer |
| 2023 | Differentiable Simulations for Enhanced Sampling of Rare Events. Martin Sípka, Johannes C. B. Dietschreit, Lukás Grajciar, Rafael Gómez-Bombarelli |
| 2023 | Differentiable Tree Operations Promote Compositional Generalization. Paul Soulos, Edward J. Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao |
| 2023 | Differentiable and Transportable Structure Learning. Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar |
| 2023 | Differential Privacy has Bounded Impact on Fairness in Classification. Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi |
| 2023 | Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models. Phillip Rust, Anders Søgaard |
| 2023 | Differentially Private Distributed Bayesian Linear Regression with MCMC. Baris Alparslan, Sinan Yildirim, S. Ilker Birbil |
| 2023 | Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang |
| 2023 | Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni |
| 2023 | Differentially Private Optimization on Large Model at Small Cost. Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, George Karypis |
| 2023 | Differentially Private Sharpness-Aware Training. Jinseong Park, Hoki Kim, Yujin Choi, Jaewook Lee |
| 2023 | Differentially Private Stochastic Convex Optimization under a Quantile Loss Function. Du Chen, Geoffrey A. Chua |
| 2023 | Diffusion Based Representation Learning. Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, Arash Mehrjou |
| 2023 | Diffusion Models are Minimax Optimal Distribution Estimators. Kazusato Oko, Shunta Akiyama, Taiji Suzuki |
| 2023 | Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? Victor Boutin, Thomas Fel, Lakshya Singhal, Rishav Mukherji, Akash Nagaraj, Julien Colin, Thomas Serre |
| 2023 | Diffusion Models for Black-Box Optimization. Siddarth Krishnamoorthy, Satvik Mehul Mashkaria, Aditya Grover |
| 2023 | Dimension-independent Certified Neural Network Watermarks via Mollifier Smoothing. Jiaxiang Ren, Yang Zhou, Jiayin Jin, Lingjuan Lyu, Da Yan |
| 2023 | Dimensionality Reduction for General KDE Mode Finding. Xinyu Luo, Christopher Musco, Cas Widdershoven |
| 2023 | Dink-Net: Neural Clustering on Large Graphs. Yue Liu, Ke Liang, Jun Xia, Sihang Zhou, Xihong Yang, Xinwang Liu, Stan Z. Li |
| 2023 | Direct Parameterization of Lipschitz-Bounded Deep Networks. Ruigang Wang, Ian R. Manchester |
| 2023 | Directed Chain Generative Adversarial Networks. Ming Min, Ruimeng Hu, Tomoyuki Ichiba |
| 2023 | Dirichlet Diffusion Score Model for Biological Sequence Generation. Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, Jian Zhou |
| 2023 | DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab |
| 2023 | Discover and Cure: Concept-aware Mitigation of Spurious Correlation. Shirley Wu, Mert Yüksekgönül, Linjun Zhang, James Zou |
| 2023 | Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. Dayou Yu, Weishi Shi, Qi Yu |
| 2023 | Discovering Object-Centric Generalized Value Functions From Pixels. Somjit Nath, Gopeshh Raaj Subbaraj, Khimya Khetarpal, Samira Ebrahimi Kahou |
| 2023 | Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models. Parth Vipul Sangani, Arjun Shashank Kashettiwar, Pritish Chakraborty, Bhuvan Reddy Gangula, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh K. Iyer, Abir De |
| 2023 | Discrete Key-Value Bottleneck. Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Curtis Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf |
| 2023 | Disentangled Generative Models for Robust Prediction of System Dynamics. Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D. Cantwell, Anil Anthony Bharath |
| 2023 | Disentangled Multi-Fidelity Deep Bayesian Active Learning. Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu |
| 2023 | Disentangled Multiplex Graph Representation Learning. Yujie Mo, Yajie Lei, Jialie Shen, Xiaoshuang Shi, Heng Tao Shen, Xiaofeng Zhu |
| 2023 | Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning. Antonio Sclocchi, Mario Geiger, Matthieu Wyart |
| 2023 | Distance Weighted Supervised Learning for Offline Interaction Data. Joey Hejna, Jensen Gao, Dorsa Sadigh |
| 2023 | Distilling Internet-Scale Vision-Language Models into Embodied Agents. Theodore R. Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, Ishita Dasgupta |
| 2023 | Distortion and Uncertainty Aware Loss for Panoramic Depth Completion. Zhiqiang Yan, Xiang Li, Kun Wang, Shuo Chen, Jun Li, Jian Yang |
| 2023 | Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. Sanae Amani, Tor Lattimore, András György, Lin Yang |
| 2023 | Distributed Linear Bandits under Communication Constraints. Sudeep Salgia, Qing Zhao |
| 2023 | Distribution Free Domain Generalization. Peifeng Tong, Wu Su, He Li, Jialin Ding, Zhan Haoxiang, Song Xi Chen |
| 2023 | Distribution Free Prediction Sets for Node Classification. Jase Clarkson |
| 2023 | Distribution-dependent McDiarmid-type Inequalities for Functions of Unbounded Interaction. Shaojie Li, Yong Liu |
| 2023 | Distributional Offline Policy Evaluation with Predictive Error Guarantees. Runzhe Wu, Masatoshi Uehara, Wen Sun |
| 2023 | Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference. Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang, Pascale Fung, Ying Nian Wu |
| 2023 | Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han |
| 2023 | Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions. Wanshan Li, Daren Wang, Alessandro Rinaldo |
| 2023 | Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat. Shantanu Ghosh, Ke Yu, Forough Arabshahi, Kayhan Batmanghelich |
| 2023 | Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling. Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, Roy Fox |
| 2023 | Do Machine Learning Models Learn Statistical Rules Inferred from Data? Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong |
| 2023 | Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks. Peng Xu, Lin Zhang, Xuanzhou Liu, Jiaqi Sun, Yue Zhao, Haiqin Yang, Bei Yu |
| 2023 | Do Perceptually Aligned Gradients Imply Robustness? Roy Ganz, Bahjat Kawar, Michael Elad |
| 2023 | Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection. Xiaohui Zhang, Jiangyan Yi, Jianhua Tao, Chenglong Wang, Chu Yuan Zhang |
| 2023 | Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark. Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Hanlin Zhang, Scott Emmons, Dan Hendrycks |
| 2023 | DoCoFL: Downlink Compression for Cross-Device Federated Learning. Ron Dorfman, Shay Vargaftik, Yaniv Ben-Itzhak, Kfir Yehuda Levy |
| 2023 | DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule. Maor Ivgi, Oliver Hinder, Yair Carmon |
| 2023 | DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Rémi Munos, Bernardo Ávila Pires, Michal Valko |
| 2023 | Does Continual Learning Equally Forget All Parameters? Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang |
| 2023 | Does Sparsity Help in Learning Misspecified Linear Bandits? Jialin Dong, Lin Yang |
| 2023 | Does a Neural Network Really Encode Symbolic Concepts? Mingjie Li, Quanshi Zhang |
| 2023 | Domain Adaptation for Time Series Under Feature and Label Shifts. Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, Marinka Zitnik |
| 2023 | Double-Weighting for Covariate Shift Adaptation. José Ignacio Segovia-Martín, Santiago Mazuelas, Anqi Liu |
| 2023 | Doubly Adversarial Federated Bandits. Jialin Yi, Milan Vojnovic |
| 2023 | Doubly Optimal No-Regret Learning in Monotone Games. Yang Cai, Weiqiang Zheng |
| 2023 | Dropout Reduces Underfitting. Zhuang Liu, Zhiqiu Xu, Joseph Jin, Zhiqiang Shen, Trevor Darrell |
| 2023 | Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions. Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte M. Deane, Yee Whye Teh |
| 2023 | Dual Focal Loss for Calibration. Linwei Tao, Minjing Dong, Chang Xu |
| 2023 | Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons. Rasmus Kjær Høier, D. Staudt, Christopher Zach |
| 2023 | DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning. Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao, Stratis Ioannidis, Yanzhi Wang, Jennifer G. Dy |
| 2023 | Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time. Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, MohammadTaghi Hajiaghayi, Peyman Jabbarzade, Morteza Monemizadeh |
| 2023 | Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao |
| 2023 | Dynamical Linear Bandits. Marco Mussi, Alberto Maria Metelli, Marcello Restelli |
| 2023 | Dynamics-inspired Neuromorphic Visual Representation Learning. Zhengqi Pei, Shuhui Wang |
| 2023 | E(n) Equivariant Message Passing Simplicial Networks. Floor Eijkelboom, Rob Hesselink, Erik J. Bekkers |
| 2023 | ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines. Siyuan Chen, Pratik Pramod Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry |
| 2023 | EF21-P and Friends: Improved Theoretical Communication Complexity for Distributed Optimization with Bidirectional Compression. Kaja Gruntkowska, Alexander Tyurin, Peter Richtárik |
| 2023 | ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models. Qinglong Tian, Xin Zhang, Jiwei Zhao |
| 2023 | EM-Network: Oracle Guided Self-distillation for Sequence Learning. Ji Won Yoon, Sunghwan Ahn, Hyeonseung Lee, Minchan Kim, Seok Min Kim, Nam Soo Kim |
| 2023 | ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation. Kaiwen Zhou, Kaizhi Zheng, Connor Pryor, Yilin Shen, Hongxia Jin, Lise Getoor, Xin Eric Wang |
| 2023 | Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao |
| 2023 | Effective Neural Topic Modeling with Embedding Clustering Regularization. Xiaobao Wu, Xinshuai Dong, Thong Thanh Nguyen, Anh Tuan Luu |
| 2023 | Effective Structured Prompting by Meta-Learning and Representative Verbalizer. Weisen Jiang, Yu Zhang, James T. Kwok |
| 2023 | Effective and Efficient Structural Inference with Reservoir Computing. Aoran Wang, Tsz Pan Tong, Jun Pang |
| 2023 | Effectively Using Public Data in Privacy Preserving Machine Learning. Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, Amir Houmansadr |
| 2023 | Efficient Algorithms for Exact Graph Matching on Correlated Stochastic Block Models with Constant Correlation. Joonhyuk Yang, Dongpil Shin, Hye Won Chung |
| 2023 | Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction. Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, Shuiwang Ji |
| 2023 | Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration. Blaise Delattre, Quentin Barthélemy, Alexandre Araujo, Alexandre Allauzen |
| 2023 | Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization. Brendan O'Donoghue |
| 2023 | Efficient Graph Field Integrators Meet Point Clouds. Krzysztof Marcin Choromanski, Arijit Sehanobish, Han Lin, Yunfan Zhao, Eli Berger, Tetiana Parshakova, Alvin Pan, David Watkins, Tianyi Zhang, Valerii Likhosherstov, Somnath Basu Roy Chowdhury, Kumar Avinava Dubey, Deepali Jain, Tamás Sarlós, Snigdha Chaturvedi, Adrian Weller |
| 2023 | Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming. Jinuk Kim, Yeonwoo Jeong, Deokjae Lee, Hyun Oh Song |
| 2023 | Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network. Yadi Cao, Menglei Chai, Minchen Li, Chenfanfu Jiang |
| 2023 | Efficient List-Decodable Regression using Batches. Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen |
| 2023 | Efficient Online Reinforcement Learning with Offline Data. Philip J. Ball, Laura Smith, Ilya Kostrikov, Sergey Levine |
| 2023 | Efficient Parametric Approximations of Neural Network Function Space Distance. Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse |
| 2023 | Efficient Personalized Federated Learning via Sparse Model-Adaptation. Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li |
| 2023 | Efficient Quantum Algorithms for Quantum Optimal Control. Xiantao Li, Chunhao Wang |
| 2023 | Efficient RL via Disentangled Environment and Agent Representations. Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak |
| 2023 | Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation. Orin Levy, Alon Cohen, Asaf B. Cassel, Yishay Mansour |
| 2023 | Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language. Alexei Baevski, Arun Babu, Wei-Ning Hsu, Michael Auli |
| 2023 | Efficient Sequence Transduction by Jointly Predicting Tokens and Durations. Hainan Xu, Fei Jia, Somshubra Majumdar, He Huang, Shinji Watanabe, Boris Ginsburg |
| 2023 | Efficient Training of Language Models using Few-Shot Learning. Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar |
| 2023 | Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification. Juan Maroñas, Daniel Hernández-Lobato |
| 2023 | Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu |
| 2023 | Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian. Haiyang Yu, Zhao Xu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji |
| 2023 | Efficient displacement convex optimization with particle gradient descent. Hadi Daneshmand, Jason D. Lee, Chi Jin |
| 2023 | Efficient preconditioned stochastic gradient descent for estimation in latent variable models. Charlotte Baey, Maud Delattre, Estelle Kuhn, Jean-Benoist Leger, Sarah Lemler |
| 2023 | Efficiently predicting high resolution mass spectra with graph neural networks. Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, Thomas Butler |
| 2023 | Eliminating Adversarial Noise via Information Discard and Robust Representation Restoration. Dawei Zhou, Yukun Chen, Nannan Wang, Decheng Liu, Xinbo Gao, Tongliang Liu |
| 2023 | Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning. Aqeel Labash, Florian Stelzer, Daniel Majoral, Raul Vicente Zafra |
| 2023 | Emergence of Sparse Representations from Noise. Trenton Bricken, Rylan Schaeffer, Bruno A. Olshausen, Gabriel Kreiman |
| 2023 | Emergent Agentic Transformer from Chain of Hindsight Experience. Hao Liu, Pieter Abbeel |
| 2023 | Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions. Mahyar Khayatkhoei, Wael Abd-Almageed |
| 2023 | Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets. Nils Kohring, Fabian Raoul Pieroth, Martin Bichler |
| 2023 | End-to-End Full-Atom Antibody Design. Xiangzhe Kong, Wenbing Huang, Yang Liu |
| 2023 | End-to-End Learning for Stochastic Optimization: A Bayesian Perspective. Yves Rychener, Daniel Kuhn, Tobias Sutter |
| 2023 | End-to-End Multi-Object Detection with a Regularized Mixture Model. Jaeyoung Yoo, Hojun Lee, Seunghyeon Seo, Inseop Chung, Nojun Kwak |
| 2023 | End-to-end Differentiable Clustering with Associative Memories. Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram |
| 2023 | End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization. Shohei Taniguchi, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo |
| 2023 | Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments. Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu |
| 2023 | Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language. Philipp Seidl, Andreu Vall, Sepp Hochreiter, Günter Klambauer |
| 2023 | Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning. Ziluo Ding, Wanpeng Zhang, Junpeng Yue, Xiangjun Wang, Tiejun Huang, Zongqing Lu |
| 2023 | Entropy-driven Unsupervised Keypoint Representation Learning in Videos. Ali Younes, Simone Schaub-Meyer, Georgia Chalvatzaki |
| 2023 | Equivariance with Learned Canonicalization Functions. Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh |
| 2023 | Equivariant Architectures for Learning in Deep Weight Spaces. Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron |
| 2023 | Equivariant Polynomials for Graph Neural Networks. Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman |
| 2023 | Escaping saddle points in zeroth-order optimization: the power of two-point estimators. Zhaolin Ren, Yujie Tang, Na Li |
| 2023 | Estimating Causal Effects using a Multi-task Deep Ensemble. Ziyang Jiang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, David E. Carlson |
| 2023 | Estimating Heterogeneous Treatment Effects: Mutual Information Bounds and Learning Algorithms. Xingzhuo Guo, Yuchen Zhang, Jianmin Wang, Mingsheng Long |
| 2023 | Estimating Joint Treatment Effects by Combining Multiple Experiments. Yonghan Jung, Jin Tian, Elias Bareinboim |
| 2023 | Estimating Possible Causal Effects with Latent Variables via Adjustment. Tian-Zuo Wang, Tian Qin, Zhi-Hua Zhou |
| 2023 | Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection. Lorenzo Perini, Paul-Christian Bürkner, Arto Klami |
| 2023 | Estimation Beyond Data Reweighting: Kernel Method of Moments. Heiner Kremer, Yassine Nemmour, Bernhard Schölkopf, Jia-Jie Zhu |
| 2023 | Evaluating Self-Supervised Learning via Risk Decomposition. Yann Dubois, Tatsunori Hashimoto, Percy Liang |
| 2023 | Evaluating Unsupervised Denoising Requires Unsupervised Metrics. Adria Marcos-Morales, Matan Leibovich, Sreyas Mohan, Joshua Lawrence Vincent, Piyush Haluai, Mai Tan, Peter A. Crozier, Carlos Fernandez-Granda |
| 2023 | Eventual Discounting Temporal Logic Counterfactual Experience Replay. Cameron Voloshin, Abhinav Verma, Yisong Yue |
| 2023 | Everyone's Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval. Hui Shi, Yupeng Gu, Yitong Zhou, Bo Zhao, Sicun Gao, Jishen Zhao |
| 2023 | Evidential Interactive Learning for Medical Image Captioning. Ervine Zheng, Qi Yu |
| 2023 | Evolving Semantic Prototype Improves Generative Zero-Shot Learning. Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang |
| 2023 | Ewald-based Long-Range Message Passing for Molecular Graphs. Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann |
| 2023 | Exact Inference in High-order Structured Prediction. Chuyang Ke, Jean Honorio |
| 2023 | Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule. Naoki Sato, Hideaki Iiduka |
| 2023 | Expectation-Complete Graph Representations with Homomorphisms. Pascal Welke, Maximilian Thiessen, Fabian Jogl, Thomas Gärtner |
| 2023 | Expected Gradients of Maxout Networks and Consequences to Parameter Initialization. Hanna Tseran, Guido Montúfar |
| 2023 | Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making. Axel Abels, Tom Lenaerts, Vito Trianni, Ann Nowé |
| 2023 | Exphormer: Sparse Transformers for Graphs. Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop |
| 2023 | Explainability as statistical inference. Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei |
| 2023 | Explainable Data-Driven Optimization: From Context to Decision and Back Again. Alexandre Forel, Axel Parmentier, Thibaut Vidal |
| 2023 | Explaining Reinforcement Learning with Shapley Values. Daniel Beechey, Thomas M. S. Smith, Özgür Simsek |
| 2023 | Explaining the effects of non-convergent MCMC in the training of Energy-Based Models. Elisabeth Agoritsas, Giovanni Catania, Aurélien Decelle, Beatriz Seoane |
| 2023 | Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization. Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, Zhi-Ming Ma |
| 2023 | Exploring Chemical Space with Score-based Out-of-distribution Generation. Seul Lee, Jaehyeong Jo, Sung Ju Hwang |
| 2023 | Exploring Model Dynamics for Accumulative Poisoning Discovery. Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han |
| 2023 | Exploring the Benefits of Training Expert Language Models over Instruction Tuning. Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, Minjoon Seo |
| 2023 | Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks. Yiwei Lu, Gautam Kamath, Yaoliang Yu |
| 2023 | Exponential Smoothing for Off-Policy Learning. Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba |
| 2023 | Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains. Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh M. Halappanavar |
| 2023 | Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms. Francesco Tonin, Alex Lambert, Panagiotis Patrinos, Johan A. K. Suykens |
| 2023 | Extrapolated Random Tree for Regression. Yuchao Cai, Yuheng Ma, Yiwei Dong, Hanfang Yang |
| 2023 | Extrapolative Controlled Sequence Generation via Iterative Refinement. Vishakh Padmakumar, Richard Yuanzhe Pang, He He, Ankur P. Parikh |
| 2023 | FAENet: Frame Averaging Equivariant GNN for Materials Modeling. Alexandre Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, David Rolnick |
| 2023 | FAIRER: Fairness as Decision Rationale Alignment. Tianlin Li, Qing Guo, Aishan Liu, Mengnan Du, Zhiming Li, Yang Liu |
| 2023 | FARE: Provably Fair Representation Learning with Practical Certificates. Nikola Jovanovic, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev |
| 2023 | FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems. Matthieu Blanke, Marc Lelarge |
| 2023 | FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation. Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon |
| 2023 | FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning. Congyu Qiao, Ning Xu, Jiaqi Lv, Yi Ren, Xin Geng |
| 2023 | FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels. Guillaume Staerman, Cédric Allain, Alexandre Gramfort, Thomas Moreau |
| 2023 | Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction. Andrey V. Savchenko |
| 2023 | Fair Densities via Boosting the Sufficient Statistics of Exponential Families. Alexander Soen, Hisham Husain, Richard Nock |
| 2023 | Fair Neighbor Embedding. Jaakko Peltonen, Wen Xu, Timo Nummenmaa, Jyrki Nummenmaa |
| 2023 | Fair and Accurate Decision Making through Group-Aware Learning. Ramtin Hosseini, Li Zhang, Bhanu Garg, Pengtao Xie |
| 2023 | Fair and Optimal Classification via Post-Processing. Ruicheng Xian, Lang Yin, Han Zhao |
| 2023 | Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning. Kwangho Kim, José R. Zubizarreta |
| 2023 | Fair yet Asymptotically Equal Collaborative Learning. Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low |
| 2023 | Fairness in Matching under Uncertainty. Siddartha Devic, David Kempe, Vatsal Sharan, Aleksandra Korolova |
| 2023 | Fairness in Streaming Submodular Maximization over a Matroid Constraint. Marwa El Halabi, Federico Fusco, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski |
| 2023 | Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning. Hongzuo Xu, Yijie Wang, Juhui Wei, Songlei Jian, Yizhou Li, Ning Liu |
| 2023 | Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization. Ameya Velingker, Maximilian Vötsch, David P. Woodruff, Samson Zhou |
| 2023 | Fast Algorithms for Distributed k-Clustering with Outliers. Junyu Huang, Qilong Feng, Ziyun Huang, Jinhui Xu, Jianxin Wang |
| 2023 | Fast Combinatorial Algorithms for Min Max Correlation Clustering. Sami Davies, Benjamin Moseley, Heather Newman |
| 2023 | Fast Excess Risk Rates via Offset Rademacher Complexity. Chenguang Duan, Yuling Jiao, Lican Kang, Xiliang Lu, Jerry Zhijian Yang |
| 2023 | Fast Federated Machine Unlearning with Nonlinear Functional Theory. Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, Jun Huan |
| 2023 | Fast Inference from Transformers via Speculative Decoding. Yaniv Leviathan, Matan Kalman, Yossi Matias |
| 2023 | Fast Online Node Labeling for Very Large Graphs. Baojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh |
| 2023 | Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control. Zhen Lin, Shubhendu Trivedi, Cao Xiao, Jimeng Sun |
| 2023 | Fast Private Kernel Density Estimation via Locality Sensitive Quantization. Tal Wagner, Yonatan Naamad, Nina Mishra |
| 2023 | Fast Rates for Maximum Entropy Exploration. Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard |
| 2023 | Fast Rates in Time-Varying Strongly Monotone Games. Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou |
| 2023 | Fast Sampling of Diffusion Models via Operator Learning. Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, Anima Anandkumar |
| 2023 | Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder |
| 2023 | Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective. Michael Eli Sander, Joan Puigcerver, Josip Djolonga, Gabriel Peyré, Mathieu Blondel |
| 2023 | Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. Lesi Chen, Jing Xu, Luo Luo |
| 2023 | Faster Rates of Convergence to Stationary Points in Differentially Private Optimization. Raman Arora, Raef Bassily, Tomás González, Cristóbal Guzmán, Michael Menart, Enayat Ullah |
| 2023 | FeDXL: Provable Federated Learning for Deep X-Risk Optimization. Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang |
| 2023 | Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation. Peifeng Gao, Qianqian Xu, Peisong Wen, Zhiyong Yang, Huiyang Shao, Qingming Huang |
| 2023 | Feature Expansion for Graph Neural Networks. Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang, Yujiu Yang |
| 2023 | Feature Programming for Multivariate Time Series Prediction. Alex Daniel Reneau, Jerry Yao-Chieh Hu, Ammar Gilani, Han Liu |
| 2023 | Feature learning in deep classifiers through Intermediate Neural Collapse. Akshay Rangamani, Marius Lindegaard, Tomer Galanti, Tomaso A. Poggio |
| 2023 | Featured Graph Coarsening with Similarity Guarantees. Manoj Kumar, Anurag Sharma, Shashwat Saxena, Sandeep Kumar |
| 2023 | Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. Jianyi Zhang, Ang Li, Minxue Tang, Jingwei Sun, Xiang Chen, Fan Zhang, Changyou Chen, Yiran Chen, Hai Li |
| 2023 | FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks. Bingqing Song, Prashant Khanduri, Xinwei Zhang, Jinfeng Yi, Mingyi Hong |
| 2023 | FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. Yongxin Guo, Xiaoying Tang, Tao Lin |
| 2023 | FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization. Hao Zhang, Chenglin Li, Wenrui Dai, Junni Zou, Hongkai Xiong |
| 2023 | FedDisco: Federated Learning with Discrepancy-Aware Collaboration. Rui Ye, Mingkai Xu, Jianyu Wang, Chenxin Xu, Siheng Chen, Yanfeng Wang |
| 2023 | FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization. Zhen Wang, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li |
| 2023 | FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models. Songze Li, Duanyi Yao, Jin Liu |
| 2023 | Federated Adversarial Learning: A Framework with Convergence Analysis. Xiaoxiao Li, Zhao Song, Jiaming Yang |
| 2023 | Federated Conformal Predictors for Distributed Uncertainty Quantification. Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar |
| 2023 | Federated Heavy Hitter Recovery under Linear Sketching. Adrià Gascón, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh |
| 2023 | Federated Linear Contextual Bandits with User-level Differential Privacy. Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hejazinia, Jing Yang |
| 2023 | Federated Online and Bandit Convex Optimization. Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro |
| 2023 | Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection. Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D. Nowak, Yixuan Li |
| 2023 | Few-Sample Feature Selection via Feature Manifold Learning. David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon |
| 2023 | Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction. Georgii Sergeevich Novikov, Daniel Bershatsky, Julia Gusak, Alex Shonenkov, Denis Valerievich Dimitrov, Ivan V. Oseledets |
| 2023 | Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation. Yeonsung Jung, Hajin Shim, June Yong Yang, Eunho Yang |
| 2023 | Finding Generalization Measures by Contrasting Signal and Noise. Jiaye Teng, Bohang Zhang, Ruichen Li, Haowei He, Yequan Wang, Yan Tian, Yang Yuan |
| 2023 | Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs. Yizhen Zheng, He Zhang, Vincent Cheng-Siong Lee, Yu Zheng, Xiao Wang, Shirui Pan |
| 2023 | Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2023 | Fisher Information Embedding for Node and Graph Learning. Dexiong Chen, Paolo Pellizzoni, Karsten M. Borgwardt |
| 2023 | Flash: Concept Drift Adaptation in Federated Learning. Kunjal Panchal, Sunav Choudhary, Subrata Mitra, Koyel Mukherjee, Somdeb Sarkhel, Saayan Mitra, Hui Guan |
| 2023 | FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU. Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Beidi Chen, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang |
| 2023 | FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization. Jung Hyun Lee, Jeonghoon Kim, Se Jung Kwon, Dongsoo Lee |
| 2023 | Flexible Phase Dynamics for Bio-Plausible Contrastive Learning. Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie |
| 2023 | Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning. Sam Lobel, Akhil Bagaria, George Konidaris |
| 2023 | For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal. Yingdong Hu, Renhao Wang, Li Erran Li, Yang Gao |
| 2023 | Forget Unlearning: Towards True Data-Deletion in Machine Learning. Rishav Chourasia, Neil Shah |
| 2023 | Formalizing Preferences Over Runtime Distributions. Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden |
| 2023 | Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space. Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim |
| 2023 | Fourmer: An Efficient Global Modeling Paradigm for Image Restoration. Man Zhou, Jie Huang, Chun-Le Guo, Chongyi Li |
| 2023 | Fractional Denoising for 3D Molecular Pre-training. Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma |
| 2023 | Free-Form Variational Inference for Gaussian Process State-Space Models. Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson |
| 2023 | From Adaptive Query Release to Machine Unlearning. Enayat Ullah, Raman Arora |
| 2023 | From Hypergraph Energy Functions to Hypergraph Neural Networks. Yuxin Wang, Quan Gan, Xipeng Qiu, Xuanjing Huang, David Wipf |
| 2023 | From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. Edwige Cyffers, Aurélien Bellet, Debabrota Basu |
| 2023 | From Perception to Programs: Regularize, Overparameterize, and Amortize. Hao Tang, Kevin Ellis |
| 2023 | From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks. Cai Zhou, Xiyuan Wang, Muhan Zhang |
| 2023 | From Robustness to Privacy and Back. Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou |
| 2023 | From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders. Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Gal Novik |
| 2023 | Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone |
| 2023 | Fully Dynamic Submodular Maximization over Matroids. Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam |
| 2023 | Fully-Adaptive Composition in Differential Privacy. Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu |
| 2023 | Function-Space Regularization in Neural Networks: A Probabilistic Perspective. Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson |
| 2023 | Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification. Florian Heinrichs, Mavin Heim, Corinna Weber |
| 2023 | Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods. Aleksandr Shevchenko, Kevin Kögler, Hamed Hassani, Marco Mondelli |
| 2023 | Fundamental Tradeoffs in Learning with Prior Information. Anirudha Majumdar |
| 2023 | FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning. Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu |
| 2023 | Future-conditioned Unsupervised Pretraining for Decision Transformer. Zhihui Xie, Zichuan Lin, Deheng Ye, Qiang Fu, Yang Wei, Shuai Li |
| 2023 | GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks. Salah Ghamizi, Jingfeng Zhang, Maxime Cordy, Mike Papadakis, Masashi Sugiyama, Yves Le Traon |
| 2023 | GC-Flow: A Graph-Based Flow Network for Effective Clustering. Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, Jie Chen |
| 2023 | GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models. Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen, Weinan Zhang, Jun Wang, Yaodong Yang, Luo Mai |
| 2023 | GFlowNet-EM for Learning Compositional Latent Variable Models. Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie E. Everett, Alexandros Graikos, Yoshua Bengio |
| 2023 | GFlowOut: Dropout with Generative Flow Networks. Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio |
| 2023 | GLOBE-CE: A Translation Based Approach for Global Counterfactual Explanations. Dan Ley, Saumitra Mishra, Daniele Magazzeni |
| 2023 | GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming. Huigen Ye, Hua Xu, Hongyan Wang, Chengming Wang, Yu Jiang |
| 2023 | GNOT: A General Neural Operator Transformer for Operator Learning. Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu |
| 2023 | GOAT: A Global Transformer on Large-scale Graphs. Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein |
| 2023 | GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets. Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta J. Bedathur |
| 2023 | GREAD: Graph Neural Reaction-Diffusion Networks. Jeongwhan Choi, Seoyoung Hong, Noseong Park, Sung-Bae Cho |
| 2023 | Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. Marc Härkönen, Markus Lange-Hegermann, Bogdan Raita |
| 2023 | Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick |
| 2023 | GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency. Minseop Kwak, Jiuhn Song, Seungryong Kim |
| 2023 | General Covariance Data Augmentation for Neural PDE Solvers. Vladimir Fanaskov, Tianchi Yu, Alexander Rudikov, Ivan V. Oseledets |
| 2023 | General Sequential Episodic Memory Model. Arjun Karuvally, Terrence J. Sejnowski, Hava T. Siegelmann |
| 2023 | Generalization Analysis for Contrastive Representation Learning. Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou |
| 2023 | Generalization Bounds using Data-Dependent Fractal Dimensions. Benjamin Dupuis, George Deligiannidis, Umut Simsekli |
| 2023 | Generalization on the Unseen, Logic Reasoning and Degree Curriculum. Emmanuel Abbe, Samy Bengio, Aryo Lotfi, Kevin Rizk |
| 2023 | Generalized Disparate Impact for Configurable Fairness Solutions in ML. Luca Giuliani, Eleonora Misino, Michele Lombardi |
| 2023 | Generalized Implicit Follow-The-Regularized-Leader. Keyi Chen, Francesco Orabona |
| 2023 | Generalized Polyak Step Size for First Order Optimization with Momentum. Xiaoyu Wang, Mikael Johansson, Tong Zhang |
| 2023 | Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost. Marina Knittel, Max Springer, John P. Dickerson, MohammadTaghi Hajiaghayi |
| 2023 | Generalized Teacher Forcing for Learning Chaotic Dynamics. Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz |
| 2023 | Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization. Ziyi Chen, Yi Zhou, Yingbin Liang, Zhaosong Lu |
| 2023 | Generalizing Neural Wave Functions. Nicholas Gao, Stephan Günnemann |
| 2023 | Generated Graph Detection. Yihan Ma, Zhikun Zhang, Ning Yu, Xinlei He, Michael Backes, Yun Shen, Yang Zhang |
| 2023 | Generating Language Corrections for Teaching Physical Control Tasks. Megha Srivastava, Noah D. Goodman, Dorsa Sadigh |
| 2023 | Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds. Yeqing Lin, Mohammed AlQuraishi |
| 2023 | Generating Private Synthetic Data with Genetic Algorithms. Terrance Liu, Jingwu Tang, Giuseppe Vietri, Steven Wu |
| 2023 | Generative Adversarial Symmetry Discovery. Jianke Yang, Robin Walters, Nima Dehmamy, Rose Yu |
| 2023 | Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting. Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley |
| 2023 | Generative Decoding of Visual Stimuli. Eleni Miliotou, Panagiotis Kyriakis, Jason D. Hinman, Andrei Irimia, Paul Bogdan |
| 2023 | Generative Graph Dictionary Learning. Zhichen Zeng, Ruike Zhu, Yinglong Xia, Hanqing Zeng, Hanghang Tong |
| 2023 | Generative Pretraining for Black-Box Optimization. Satvik Mehul Mashkaria, Siddarth Krishnamoorthy, Aditya Grover |
| 2023 | Geometric Autoencoders - What You See is What You Decode. Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht |
| 2023 | Geometric Clifford Algebra Networks. David Ruhe, Jayesh K. Gupta, Steven De Keninck, Max Welling, Johannes Brandstetter |
| 2023 | Geometric Latent Diffusion Models for 3D Molecule Generation. Minkai Xu, Alexander S. Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec |
| 2023 | GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration. Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon |
| 2023 | Gibbsian Polar Slice Sampling. Philip Schär, Michael Habeck, Daniel Rudolf |
| 2023 | Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. Nikhil Kandpal, Brian Lester, Mohammed Muqeeth, Anisha Mascarenhas, Monty Evans, Vishal Baskaran, Tenghao Huang, Haokun Liu, Colin Raffel |
| 2023 | Global Context Vision Transformers. Ali Hatamizadeh, Hongxu Yin, Greg Heinrich, Jan Kautz, Pavlo Molchanov |
| 2023 | Global Optimization with Parametric Function Approximation. Chong Liu, Yu-Xiang Wang |
| 2023 | Global Selection of Contrastive Batches via Optimization on Sample Permutations. Vin Sachidananda, Ziyi Yang, Chenguang Zhu |
| 2023 | Global optimality for Euclidean CCCP under Riemannian convexity. Melanie Weber, Suvrit Sra |
| 2023 | Global optimality of Elman-type RNNs in the mean-field regime. Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee |
| 2023 | Go Beyond Imagination: Maximizing Episodic Reachability with World Models. Yao Fu, Run Peng, Honglak Lee |
| 2023 | Gradient Descent Converges Linearly for Logistic Regression on Separable Data. Kyriakos Axiotis, Maxim Sviridenko |
| 2023 | Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks. Yihang Gao, Yiqi Gu, Michael Ng |
| 2023 | Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond. Itai Kreisler, Mor Shpigel Nacson, Daniel Soudry, Yair Carmon |
| 2023 | Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space. Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh |
| 2023 | Gradient-Free Structured Pruning with Unlabeled Data. Azade Nova, Hanjun Dai, Dale Schuurmans |
| 2023 | Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space. Weitang Liu, Yi-Zhuang You, Ying-Wai Li, Jingbo Shang |
| 2023 | Graph Contrastive Backdoor Attacks. Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, Dinghao Wu |
| 2023 | Graph Generative Model for Benchmarking Graph Neural Networks. Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov |
| 2023 | Graph Inductive Biases in Transformers without Message Passing. Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip H. S. Torr, Ser-Nam Lim |
| 2023 | Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication. Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang |
| 2023 | Graph Mixup with Soft Alignments. Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, Na Zou |
| 2023 | Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure. Ryoma Sato |
| 2023 | Graph Neural Networks with Learnable and Optimal Polynomial Bases. Yuhe Guo, Zhewei Wei |
| 2023 | Graph Neural Tangent Kernel: Convergence on Large Graphs. Sanjukta Krishnagopal, Luana Ruiz |
| 2023 | Graph Positional Encoding via Random Feature Propagation. Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron |
| 2023 | Graph Reinforcement Learning for Network Control via Bi-Level Optimization. Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira |
| 2023 | Graph Switching Dynamical Systems. Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Efstratios Gavves |
| 2023 | GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks. Yuwen Li, Miao Xiong, Bryan Hooi |
| 2023 | Graphically Structured Diffusion Models. Christian Dietrich Weilbach, William Harvey, Frank Wood |
| 2023 | Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement. Ailin Deng, Miao Xiong, Bryan Hooi |
| 2023 | Grounding Language Models to Images for Multimodal Inputs and Outputs. Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried |
| 2023 | Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning. Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, Pierre-Yves Oudeyer |
| 2023 | Group Equivariant Fourier Neural Operators for Partial Differential Equations. Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji |
| 2023 | GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. Hanxiao Chen, Meng Hao, Hongwei Li, Kangjie Chen, Guowen Xu, Tianwei Zhang, Xilin Zhang |
| 2023 | Guiding Pretraining in Reinforcement Learning with Large Language Models. Yuqing Du, Olivia Watkins, Zihan Wang, Cédric Colas, Trevor Darrell, Pieter Abbeel, Abhishek Gupta, Jacob Andreas |
| 2023 | H-Consistency Bounds for Pairwise Misranking Loss Surrogates. Anqi Mao, Mehryar Mohri, Yutao Zhong |
| 2023 | H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features. Hangbin Lee, Youngjo Lee |
| 2023 | HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption. Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, Mun-Kyu Lee |
| 2023 | HOPE: High-order Graph ODE For Modeling Interacting Dynamics. Xiao Luo, Jingyang Yuan, Zijie Huang, Huiyu Jiang, Yifang Qin, Wei Ju, Ming Zhang, Yizhou Sun |
| 2023 | Half-Hop: A graph upsampling approach for slowing down message passing. Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer |
| 2023 | Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. Dylan J. Foster, Noah Golowich, Sham M. Kakade |
| 2023 | Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing. Aditya Desai, Keren Zhou, Anshumali Shrivastava |
| 2023 | Harmonic Neural Networks. Atiyo Ghosh, Antonio Andrea Gentile, Mario Dagrada, Chul Lee, Seong-Hyok Sean Kim, Hyukgeun Cha, Yunjun Choi, Dongho Kim, Jeong-Il Kye, Vincent Emanuel Elfving |
| 2023 | HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang |
| 2023 | Hidden Symmetries of ReLU Networks. J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick |
| 2023 | Hiding Data Helps: On the Benefits of Masking for Sparse Coding. Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge |
| 2023 | Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles. Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer |
| 2023 | Hierarchical Diffusion for Offline Decision Making. Wenhao Li, Xiangfeng Wang, Bo Jin, Hongyuan Zha |
| 2023 | Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction. Minghao Guo, Veronika Thost, Samuel W. Song, Adithya Balachandran, Payel Das, Jie Chen, Wojciech Matusik |
| 2023 | Hierarchical Imitation Learning with Vector Quantized Models. Kalle Kujanpää, Joni Pajarinen, Alexander Ilin |
| 2023 | Hierarchical Neural Coding for Controllable CAD Model Generation. Xiang Xu, Pradeep Kumar Jayaraman, Joseph George Lambourne, Karl D. D. Willis, Yasutaka Furukawa |
| 2023 | Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs. Guan-Ting Liu, En-Pei Hu, Pu-Jen Cheng, Hung-yi Lee, Shao-Hua Sun |
| 2023 | Hierarchies of Reward Machines. Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo |
| 2023 | High Fidelity Image Counterfactuals with Probabilistic Causal Models. Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker |
| 2023 | High Probability Convergence of Stochastic Gradient Methods. Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen, Alina Ene, Huy L. Nguyen |
| 2023 | High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik |
| 2023 | High-dimensional Clustering onto Hamiltonian Cycle. Tianyi Huang, Shenghui Cheng, Stan Z. Li, Zhengjun Zhang |
| 2023 | High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors. Shivam Gupta, Jasper C. H. Lee, Eric Price |
| 2023 | Hindsight Learning for MDPs with Exogenous Inputs. Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo de Oliveira Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan |
| 2023 | Homomorphism AutoEncoder - Learning Group Structured Representations from Observed Transitions. Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf |
| 2023 | Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes. Runlong Zhou, Ruosong Wang, Simon Shaolei Du |
| 2023 | Horizon-free Learning for Markov Decision Processes and Games: Stochastically Bounded Rewards and Improved Bounds. Shengshi Li, Lin Yang |
| 2023 | How Bad is Top-K Recommendation under Competing Content Creators? Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu |
| 2023 | How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding. Yuchen Li, Yuanzhi Li, Andrej Risteski |
| 2023 | How Does Information Bottleneck Help Deep Learning? Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang |
| 2023 | How Jellyfish Characterise Alternating Group Equivariant Neural Networks. Edward Pearce-Crump |
| 2023 | How Many Perturbations Break This Model? Evaluating Robustness Beyond Adversarial Accuracy. Raphaël Olivier, Bhiksha Raj |
| 2023 | How Powerful are Shallow Neural Networks with Bandlimited Random Weights? Ming Li, Sho Sonoda, Feilong Cao, Yu Guang Wang, Jiye Liang |
| 2023 | How much does Initialization Affect Generalization? Sameera Ramasinghe, Lachlan Ewen MacDonald, Moshiur R. Farazi, Hemanth Saratchandran, Simon Lucey |
| 2023 | How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control. Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman, Jeremias Sulam |
| 2023 | How to address monotonicity for model risk management? Dangxing Chen, Weicheng Ye |
| 2023 | Human-Timescale Adaptation in an Open-Ended Task Space. Jakob Bauer, Kate Baumli, Feryal M. P. Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M. Zhang |
| 2023 | Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection. Marc Lafon, Elias Ramzi, Clément Rambour, Nicolas Thome |
| 2023 | Hyena Hierarchy: Towards Larger Convolutional Language Models. Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré |
| 2023 | HyperTuning: Toward Adapting Large Language Models without Back-propagation. Jason Phang, Yi Mao, Pengcheng He, Weizhu Chen |
| 2023 | Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning. Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon |
| 2023 | Hyperbolic Image-text Representations. Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Shanmukha Ramakrishna Vedantam |
| 2023 | Hyperbolic Representation Learning: Revisiting and Advancing. Menglin Yang, Min Zhou, Rex Ying, Yankai Chen, Irwin King |
| 2023 | Hyperparameters in Reinforcement Learning and How To Tune Them. Theresa Eimer, Marius Lindauer, Roberta Raileanu |
| 2023 | Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. Samuel Daulton, Maximilian Balandat, Eytan Bakshy |
| 2023 | Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. Anass Aghbalou, Guillaume Staerman |
| 2023 | I Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A. Theodorou, Weili Nie, Anima Anandkumar |
| 2023 | ILLUME: Rationalizing Vision-Language Models through Human Interactions. Manuel Brack, Patrick Schramowski, Björn Deiseroth, Kristian Kersting |
| 2023 | IRNeXt: Rethinking Convolutional Network Design for Image Restoration. Yuning Cui, Wenqi Ren, Sining Yang, Xiaochun Cao, Alois Knoll |
| 2023 | Identifiability and Generalizability in Constrained Inverse Reinforcement Learning. Andreas Schlaginhaufen, Maryam Kamgarpour |
| 2023 | Identifiability of Label Noise Transition Matrix. Yang Liu, Hao Cheng, Kun Zhang |
| 2023 | Identification of the Adversary from a Single Adversarial Example. Minhao Cheng, Rui Min, Haochen Sun, Pin-Yu Chen |
| 2023 | Identifying Interpretable Subspaces in Image Representations. Neha Mukund Kalibhat, Shweta Bhardwaj, C. Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi |
| 2023 | Identifying Useful Learnwares for Heterogeneous Label Spaces. Lan-Zhe Guo, Zhi Zhou, Yufeng Li, Zhi-Hua Zhou |
| 2023 | Image Restoration with Mean-Reverting Stochastic Differential Equations. Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön |
| 2023 | Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression. Zhuoran Liu, Zhengyu Zhao, Martha A. Larson |
| 2023 | Image generation with shortest path diffusion. Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, Fengting Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia |
| 2023 | Implicit Graph Neural Networks: A Monotone Operator Viewpoint. Justin M. Baker, Qingsong Wang, Cory D. Hauck, Bao Wang |
| 2023 | Implicit Jacobian regularization weighted with impurity of probability output. Sungyoon Lee, Jinseong Park, Jaewook Lee |
| 2023 | Implicit Neural Spatial Representations for Time-dependent PDEs. Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen |
| 2023 | Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. Mo Zhou, Rong Ge |
| 2023 | Importance Weighted Expectation-Maximization for Protein Sequence Design. Zhenqiao Song, Lei Li |
| 2023 | Improved Active Multi-Task Representation Learning via Lasso. Yiping Wang, Yifang Chen, Kevin Jamieson, Simon Shaolei Du |
| 2023 | Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback. Wonyoung Kim, Garud Iyengar, Assaf Zeevi |
| 2023 | Improved Algorithms for White-Box Adversarial Streams. Ying Feng, David P. Woodruff |
| 2023 | Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions. Hongrui Chen, Holden Lee, Jianfeng Lu |
| 2023 | Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis. Yongho Shin, Changyeol Lee, Gukryeol Lee, Hyung-Chan An |
| 2023 | Improved Online Conformal Prediction via Strongly Adaptive Online Learning. Aadyot Bhatnagar, Huan Wang, Caiming Xiong, Yu Bai |
| 2023 | Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. Zhe Feng, Christopher Liaw, Zixin Zhou |
| 2023 | Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation. Aditya Mate, Bryan Wilder, Aparna Taneja, Milind Tambe |
| 2023 | Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation. Uri Sherman, Tomer Koren, Yishay Mansour |
| 2023 | Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs. Kaiwen Zheng, Cheng Lu, Jianfei Chen, Jun Zhu |
| 2023 | Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process. Yidong Ouyang, Liyan Xie, Guang Cheng |
| 2023 | Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples. Dongyoon Yang, Insung Kong, Yongdai Kim |
| 2023 | Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics. Zonghan Yang, Peng Li, Tianyu Pang, Yang Liu |
| 2023 | Improving Bi-level Optimization Based Methods with Inspiration from Humans' Classroom Study Techniques. Pengtao Xie |
| 2023 | Improving Expert Predictions with Conformal Prediction. Eleni Straitouri, Lequn Wang, Nastaran Okati, Manuel Gomez Rodriguez |
| 2023 | Improving Fair Training under Correlation Shifts. Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh |
| 2023 | Improving Graph Generation by Restricting Graph Bandwidth. Nathaniel Lee Diamant, Alex M. Tseng, Kangway V. Chuang, Tommaso Biancalani, Gabriele Scalia |
| 2023 | Improving Graph Neural Networks with Learnable Propagation Operators. Moshe Eliasof, Lars Ruthotto, Eran Treister |
| 2023 | Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models. Rui Li, S. T. John, Arno Solin |
| 2023 | Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling. Xinlu Zhang, Shiyang Li, Zhiyu Chen, Xifeng Yan, Linda Ruth Petzold |
| 2023 | Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich, Hervé Jégou, Jakob Verbeek |
| 2023 | Improving Visual Prompt Tuning for Self-supervised Vision Transformers. Seungryong Yoo, Eunji Kim, Dahuin Jung, Jungbeom Lee, Sungroh Yoon |
| 2023 | Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints. Václav Vorácek, Matthias Hein |
| 2023 | Improving the Model Consistency of Decentralized Federated Learning. Yifan Shi, Li Shen, Kang Wei, Yan Sun, Bo Yuan, Xueqian Wang, Dacheng Tao |
| 2023 | In Search for a Generalizable Method for Source Free Domain Adaptation. Malik Boudiaf, Tom Denton, Bart van Merrienboer, Vincent Dumoulin, Eleni Triantafillou |
| 2023 | In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. Alicia Curth, Mihaela van der Schaar |
| 2023 | In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. Julian Bitterwolf, Maximilian Müller, Matthias Hein |
| 2023 | InGram: Inductive Knowledge Graph Embedding via Relation Graphs. Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang |
| 2023 | IncDSI: Incrementally Updatable Document Retrieval. Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, Kilian Q. Weinberger |
| 2023 | Incentivizing Exploration with Linear Contexts and Combinatorial Actions. Mark Sellke |
| 2023 | Individually Fair Learning with One-Sided Feedback. Yahav Bechavod, Aaron Roth |
| 2023 | Inferring Relational Potentials in Interacting Systems. Armand Comas Massague, Yilun Du, Christian Fernandez Lopez, Sandesh Ghimire, Mario Sznaier, Joshua B. Tenenbaum, Octavia I. Camps |
| 2023 | Infinite Action Contextual Bandits with Reusable Data Exhaust. Mark Rucker, Yinglun Zhu, Paul Mineiro |
| 2023 | Inflow, Outflow, and Reciprocity in Machine Learning. Mukund Sundararajan, Walid Krichene |
| 2023 | InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov |
| 2023 | InfoOT: Information Maximizing Optimal Transport. Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis |
| 2023 | Information-Theoretic State Space Model for Multi-View Reinforcement Learning. HyeongJoo Hwang, Seokin Seo, Youngsoo Jang, Sungyoon Kim, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim |
| 2023 | Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning. Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas |
| 2023 | Input Perturbation Reduces Exposure Bias in Diffusion Models. Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, Rita Cucchiara |
| 2023 | Input uncertainty propagation through trained neural networks. Paul Monchot, Loic Coquelin, Sébastien Julien Petit, Sébastien Marmin, Erwan Le Pennec, Nicolas Fischer |
| 2023 | Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models. Ajay Kumar Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang |
| 2023 | Instrumental Variable Estimation of Average Partial Causal Effects. Yuta Kawakami, Manabu Kuroki, Jin Tian |
| 2023 | Integrating Prior Knowledge in Contrastive Learning with Kernel. Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori |
| 2023 | Interactive Object Placement with Reinforcement Learning. Shengping Zhang, Quanling Meng, Qinglin Liu, Liqiang Nie, Bineng Zhong, Xiaopeng Fan, Rongrong Ji |
| 2023 | Internally Rewarded Reinforcement Learning. Mengdi Li, Xufeng Zhao, Jae Hee Lee, Cornelius Weber, Stefan Wermter |
| 2023 | International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, Jonathan Scarlett |
| 2023 | Internet Explorer: Targeted Representation Learning on the Open Web. Alexander Cong Li, Ellis Langham Brown, Alexei A. Efros, Deepak Pathak |
| 2023 | Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics. Jiacheng Zhu, Jielin Qiu, Aritra Guha, Zhuolin Yang, XuanLong Nguyen, Bo Li, Ding Zhao |
| 2023 | Interpretable Neural-Symbolic Concept Reasoning. Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lio, Frédéric Precioso, Mateja Jamnik, Giuseppe Marra |
| 2023 | Interval Bound Interpolation for Few-shot Learning with Few Tasks. Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty, Swagatam Das |
| 2023 | Interventional Causal Representation Learning. Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio |
| 2023 | Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs. Raif M. Rustamov, Subhabrata Majumdar |
| 2023 | Invariance in Policy Optimisation and Partial Identifiability in Reward Learning. Joar Max Viktor Skalse, Matthew Farrugia-Roberts, Stuart Russell, Alessandro Abate, Adam Gleave |
| 2023 | Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames. Ondrej Biza, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Thomas Kipf |
| 2023 | Inverse Reinforcement Learning without Reinforcement Learning. Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu |
| 2023 | Investigating the Role of Model-Based Learning in Exploration and Transfer. Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B. Hamrick |
| 2023 | Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks? Dmitry Metelev, Alexander Rogozin, Dmitry Kovalev, Alexander V. Gasnikov |
| 2023 | Is Learning Summary Statistics Necessary for Likelihood-free Inference? Yanzhi Chen, Michael U. Gutmann, Adrian Weller |
| 2023 | Is Overfitting Necessary for Implicit Video Representation? Hee Min Choi, Hyoa Kang, Dokwan Oh |
| 2023 | Iterative Approximate Cross-Validation. Yuetian Luo, Zhimei Ren, Rina Barber |
| 2023 | JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift. Drew Prinster, Suchi Saria, Anqi Liu |
| 2023 | Jump-Start Reinforcement Learning. Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman |
| 2023 | K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs. Andrea Coletta, Svitlana Vyetrenko, Tucker Balch |
| 2023 | KDEformer: Accelerating Transformers via Kernel Density Estimation. Amir Zandieh, Insu Han, Majid Daliri, Amin Karbasi |
| 2023 | Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network. Marie Guyomard, Susana Barbosa, Lionel Fillatre |
| 2023 | Kernel QuantTree. Diego Stucchi, Paolo Rizzo, Nicolò Folloni, Giacomo Boracchi |
| 2023 | Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation. Junyoung Park, Jeongyoun Ahn, Cheolwoo Park |
| 2023 | LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. Timothy Castiglia, Yi Zhou, Shiqiang Wang, Swanand Kadhe, Nathalie Baracaldo, Stacy Patterson |
| 2023 | LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework. Woojun Kim, Jeonghye Kim, Youngchul Sung |
| 2023 | LEVER: Learning to Verify Language-to-Code Generation with Execution. Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida I. Wang, Xi Victoria Lin |
| 2023 | LIV: Language-Image Representations and Rewards for Robotic Control. Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman |
| 2023 | LSDS++ : Dual Sampling for Accelerated k-means++. Chenglin Fan, Ping Li, Xiaoyun Li |
| 2023 | Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity. Dixian Zhu, Yiming Ying, Tianbao Yang |
| 2023 | Label differential privacy and private training data release. Róbert Istvan Busa-Fekete, Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii |
| 2023 | Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning. Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra |
| 2023 | Language Instructed Reinforcement Learning for Human-AI Coordination. Hengyuan Hu, Dorsa Sadigh |
| 2023 | Large Language Models Can Be Easily Distracted by Irrelevant Context. Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, Denny Zhou |
| 2023 | Large Language Models Struggle to Learn Long-Tail Knowledge. Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, Colin Raffel |
| 2023 | Last Switch Dependent Bandits with Monotone Payoff Functions. Ayoub Foussoul, Vineet Goyal, Orestis Papadigenopoulos, Assaf Zeevi |
| 2023 | Latent Traversals in Generative Models as Potential Flows. Yue Song, T. Anderson Keller, Nicu Sebe, Max Welling |
| 2023 | Layered State Discovery for Incremental Autonomous Exploration. Liyu Chen, Andrea Tirinzoni, Alessandro Lazaric, Matteo Pirotta |
| 2023 | Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning. Boyin Liu, Zhiqiang Pu, Yi Pan, Jianqiang Yi, Yanyan Liang, Du Zhang |
| 2023 | LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation. Rui Xue, Haoyu Han, MohamadAli Torkamani, Jian Pei, Xiaorui Liu |
| 2023 | LeadFL: Client Self-Defense against Model Poisoning in Federated Learning. Chaoyi Zhu, Stefanie Roos, Lydia Y. Chen |
| 2023 | Learn to Accumulate Evidence from All Training Samples: Theory and Practice. Deep Shankar Pandey, Qi Yu |
| 2023 | Learnability and Algorithm for Continual Learning. Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Bing Liu |
| 2023 | Learning Affinity with Hyperbolic Representation for Spatial Propagation. Jin-Hwi Park, Jaesung Choe, Inhwan Bae, Hae-Gon Jeon |
| 2023 | Learning Antidote Data to Individual Unfairness. Peizhao Li, Ethan Xia, Hongfu Liu |
| 2023 | Learning Belief Representations for Partially Observable Deep RL. Andrew Wang, Andrew C. Li, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith |
| 2023 | Learning Compiler Pass Orders using Coreset and Normalized Value Prediction. Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh James Leather, Yuandong Tian |
| 2023 | Learning Control by Iterative Inversion. Gal Leibovich, Guy Jacob, Or Avner, Gal Novik, Aviv Tamar |
| 2023 | Learning Control-Oriented Dynamical Structure from Data. Spencer M. Richards, Jean-Jacques E. Slotine, Navid Azizan, Marco Pavone |
| 2023 | Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows. Seobin Park, Dongjin Kim, Sungyong Baik, Tae Hyun Kim |
| 2023 | Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic. Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa |
| 2023 | Learning Deep Time-index Models for Time Series Forecasting. Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven C. H. Hoi |
| 2023 | Learning Dense Correspondences between Photos and Sketches. Xuanchen Lu, Xiaolong Wang, Judith E. Fan |
| 2023 | Learning Distributions over Quantum Measurement Outcomes. Weiyuan Gong, Scott Aaronson |
| 2023 | Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation. Yiming Cui, Linjie Yang, Haichao Yu |
| 2023 | Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks. Dominik Schnaus, Jongseok Lee, Daniel Cremers, Rudolph Triebel |
| 2023 | Learning Functional Distributions with Private Labels. Changlong Wu, Yifan Wang, Ananth Grama, Wojciech Szpankowski |
| 2023 | Learning GFlowNets From Partial Episodes For Improved Convergence And Stability. Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Cristian Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin |
| 2023 | Learning Globally Smooth Functions on Manifolds. Juan Cerviño, Luiz F. O. Chamon, Benjamin David Haeffele, René Vidal, Alejandro Ribeiro |
| 2023 | Learning Hidden Markov Models When the Locations of Missing Observations are Unknown. Binyamin Perets, Mark Kozdoba, Shie Mannor |
| 2023 | Learning Instance-Specific Augmentations by Capturing Local Invariances. Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim |
| 2023 | Learning Intuitive Policies Using Action Features. Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster |
| 2023 | Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation. Shengcao Cao, Mengtian Li, James Hays, Deva Ramanan, Yu-Xiong Wang, Liangyan Gui |
| 2023 | Learning Mixtures of Gaussians with Censored Data. Wai Ming Tai, Bryon Aragam |
| 2023 | Learning Mixtures of Markov Chains and MDPs. Chinmaya Kausik, Kevin Tan, Ambuj Tewari |
| 2023 | Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics. Pingchuan Ma, Peter Yichen Chen, Bolei Deng, Joshua B. Tenenbaum, Tao Du, Chuang Gan, Wojciech Matusik |
| 2023 | Learning Neural PDE Solvers with Parameter-Guided Channel Attention. Makoto Takamoto, Francesco Alesiani, Mathias Niepert |
| 2023 | Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. Antoine Dedieu, Guangyao Zhou, Dileep George, Miguel Lázaro-Gredilla |
| 2023 | Learning Perturbations to Explain Time Series Predictions. Joseph Enguehard |
| 2023 | Learning Physical Models that Can Respect Conservation Laws. Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney |
| 2023 | Learning Preconditioners for Conjugate Gradient PDE Solvers. Yichen Li, Peter Yichen Chen, Tao Du, Wojciech Matusik |
| 2023 | Learning Prescriptive ReLU Networks. Wei Sun, Asterios Tsiourvas |
| 2023 | Learning Rate Schedules in the Presence of Distribution Shift. Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah |
| 2023 | Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation. Fengxue Zhang, Jialin Song, James C. Bowden, Alexander Ladd, Yisong Yue, Thomas Desautels, Yuxin Chen |
| 2023 | Learning Representations without Compositional Assumptions. Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar |
| 2023 | Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping. Baorui Ma, Yu-Shen Liu, Zhizhong Han |
| 2023 | Learning Subpocket Prototypes for Generalizable Structure-based Drug Design. Zaixi Zhang, Qi Liu |
| 2023 | Learning Temporally AbstractWorld Models without Online Experimentation. Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset |
| 2023 | Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator. Sicheng Zhu, Bang An, Furong Huang, Sanghyun Hong |
| 2023 | Learning Unnormalized Statistical Models via Compositional Optimization. Wei Jiang, Jiayu Qin, Lingyu Wu, Changyou Chen, Tianbao Yang, Lijun Zhang |
| 2023 | Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees. Pengfei Li, Jianyi Yang, Shaolei Ren |
| 2023 | Learning in POMDPs is Sample-Efficient with Hindsight Observability. Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang |
| 2023 | Learning the Dynamics of Sparsely Observed Interacting Systems. Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot, Agathe Guilloux |
| 2023 | Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs. Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco |
| 2023 | Learning to Bid in Repeated First-Price Auctions with Budgets. Qian Wang, Zongjun Yang, Xiaotie Deng, Yuqing Kong |
| 2023 | Learning to Boost Training by Periodic Nowcasting Near Future Weights. Jinhyeok Jang, Woo-han Yun, Won Hwa Kim, Youngwoo Yoon, Jaehong Kim, Jaeyeon Lee, ByungOk Han |
| 2023 | Learning to Decouple Complex Systems. Zihan Zhou, Tianshu Yu |
| 2023 | Learning to Design Analog Circuits to Meet Threshold Specifications. Dmitrii Krylov, Pooya Khajeh, Junhan Ouyang, Thomas Reeves, Tongkai Liu, Hiba Ajmal, Hamidreza Aghasi, Roy Fox |
| 2023 | Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model. Siyu Chen, Jibang Wu, Yifan Wu, Zhuoran Yang |
| 2023 | Learning to Initiate and Reason in Event-Driven Cascading Processes. Yuval Atzmon, Eli A. Meirom, Shie Mannor, Gal Chechik |
| 2023 | Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling. Tianqi Chen, Mingyuan Zhou |
| 2023 | Learning to Learn from APIs: Black-Box Data-Free Meta-Learning. Zixuan Hu, Li Shen, Zhenyi Wang, Baoyuan Wu, Chun Yuan, Dacheng Tao |
| 2023 | Learning to Maximize Mutual Information for Dynamic Feature Selection. Ian Connick Covert, Wei Qiu, Mingyu Lu, Nayoon Kim, Nathan J. White, Su-In Lee |
| 2023 | Learning to Optimize Differentiable Games. Xuxi Chen, Nelson Vadori, Tianlong Chen, Zhangyang Wang |
| 2023 | Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement. Eden Saig, Nir Rosenfeld |
| 2023 | Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning. Thomas Miconi |
| 2023 | Learning useful representations for shifting tasks and distributions. Jianyu Zhang, Léon Bottou |
| 2023 | Learning-Rate-Free Learning by D-Adaptation. Aaron Defazio, Konstantin Mishchenko |
| 2023 | Learning-augmented private algorithms for multiple quantile release. Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii |
| 2023 | LegendreTron: Uprising Proper Multiclass Loss Learning. Kevin H. Lam, Christian J. Walder, Spiridon I. Penev, Richard Nock |
| 2023 | Less is More: Task-aware Layer-wise Distillation for Language Model Compression. Chen Liang, Simiao Zuo, Qingru Zhang, Pengcheng He, Weizhu Chen, Tuo Zhao |
| 2023 | Leveraging Demonstrations to Improve Online Learning: Quality Matters. Botao Hao, Rahul Jain, Tor Lattimore, Benjamin Van Roy, Zheng Wen |
| 2023 | Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks. Feng Ji, See Hian Lee, Hanyang Meng, Kai Zhao, Jielong Yang, Wee Peng Tay |
| 2023 | Leveraging Offline Data in Online Reinforcement Learning. Andrew Wagenmaker, Aldo Pacchiano |
| 2023 | Leveraging Proxy of Training Data for Test-Time Adaptation. Juwon Kang, Nayeong Kim, Donghyeon Kwon, Jungseul Ok, Suha Kwak |
| 2023 | Lifelong Language Pretraining with Distribution-Specialized Experts. Wuyang Chen, Yanqi Zhou, Nan Du, Yanping Huang, James Laudon, Zhifeng Chen, Claire Cui |
| 2023 | Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data. Yubo Zhuang, Xiaohui Chen, Yun Yang |
| 2023 | LinSATNet: The Positive Linear Satisfiability Neural Networks. Runzhong Wang, Yunhao Zhang, Ziao Guo, Tianyi Chen, Xiaokang Yang, Junchi Yan |
| 2023 | Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies. Hannah Pinson, Joeri Lenaerts, Vincent Ginis |
| 2023 | Linear Causal Disentanglement via Interventions. Chandler Squires, Anna Seigal, Salil S. Bhate, Caroline Uhler |
| 2023 | Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains. Matthew Dowling, Yuan Zhao, Il Memming Park |
| 2023 | Linear optimal partial transport embedding. Yikun Bai, Ivan Vladimir Medri, Rocio Diaz Martin, Rana Muhammad Shahroz Khan, Soheil Kolouri |
| 2023 | Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach. Prashant Khanduri, Ioannis C. Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu, Jiawei Zhang, Mingyi Hong |
| 2023 | Linkless Link Prediction via Relational Distillation. Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao |
| 2023 | LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control. Xujie Song, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Chen Chen, Bo Cheng, Bo Zhang, Junqing Wei, Xiaoming Simon Wang |
| 2023 | Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy. Xiyao Wang, Wichayaporn Wongkamjan, Ruonan Jia, Furong Huang |
| 2023 | LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation. Yixiao Li, Yifan Yu, Qingru Zhang, Chen Liang, Pengcheng He, Weizhu Chen, Tuo Zhao |
| 2023 | Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee |
| 2023 | Local Vertex Colouring Graph Neural Networks. Shouheng Li, Dongwoo Kim, Qing Wang |
| 2023 | Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed! Avik Pal, Alan Edelman, Christopher Vincent Rackauckas |
| 2023 | Long Horizon Temperature Scaling. Andy Shih, Dorsa Sadigh, Stefano Ermon |
| 2023 | Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels. Min-Kook Suh, Seung-Woo Seo |
| 2023 | Long-Term Rhythmic Video Soundtracker. Jiashuo Yu, Yaohui Wang, Xinyuan Chen, Xiao Sun, Yu Qiao |
| 2023 | LongCoder: A Long-Range Pre-trained Language Model for Code Completion. Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian J. McAuley |
| 2023 | Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers. Grant P. Strimel, Yi Xie, Brian John King, Martin Radfar, Ariya Rastrow, Athanasios Mouchtaris |
| 2023 | LookupFFN: Making Transformers Compute-lite for CPU inference. Zhanpeng Zeng, Michael Davies, Pranav Pulijala, Karthikeyan Sankaralingam, Vikas Singh |
| 2023 | Looped Transformers as Programmable Computers. Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos |
| 2023 | Loss Balancing for Fair Supervised Learning. Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan |
| 2023 | Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation. Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, Arash Vahdat |
| 2023 | Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability. Robert Tjarko Lange, Henning Sprekeler |
| 2023 | Low Complexity Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Optimization over (Non-)Convex Set. Enming Liang, Minghua Chen, Steven H. Low |
| 2023 | Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling. Yunfan Li, Yiran Wang, Yu Cheng, Lin Yang |
| 2023 | Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single. Paul Vicol |
| 2023 | Lower Bounds for Learning in Revealing POMDPs. Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai |
| 2023 | Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit. Yeonju Ro, Zhangyang Wang, Vijay Chidambaram, Aditya Akella |
| 2023 | MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior. Jennifer J. Sun, Markus Marks, Andrew Wesley Ulmer, Dipam Chakraborty, Brian Geuther, Edward Hayes, Heng Jia, Vivek Kumar, Sebastian Oleszko, Zachary Partridge, Milan Peelman, Alice Robie, Catherine E. Schretter, Keith Sheppard, Chao Sun, Param Uttarwar, Julian Morgan Wagner, Erik Werner, Joseph Parker, Pietro Perona, Yisong Yue, Kristin Branson, Ann Kennedy |
| 2023 | MAGANet: Achieving Combinatorial Generalization by Modeling a Group Action. Geonho Hwang, Jaewoong Choi, Hyunsoo Cho, Myungjoo Kang |
| 2023 | MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations. Anqi Li, Byron Boots, Ching-An Cheng |
| 2023 | MANSA: Learning Fast and Slow in Multi-Agent Systems. David Henry Mguni, Haojun Chen, Taher Jafferjee, Jianhong Wang, Longfei Yue, Xidong Feng, Stephen Marcus McAleer, Feifei Tong, Jun Wang, Yaodong Yang |
| 2023 | MEWL: Few-shot multimodal word learning with referential uncertainty. Guangyuan Jiang, Manjie Xu, Shiji Xin, Wei Liang, Yujia Peng, Chi Zhang, Yixin Zhu |
| 2023 | MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods. Ali Taghibakhshi, Nicolas Nytko, Tareq Uz Zaman, Scott P. MacLachlan, Luke N. Olson, Matthew West |
| 2023 | MODeL: Memory Optimizations for Deep Learning. Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty |
| 2023 | Machine Learning Force Fields with Data Cost Aware Training. Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao |
| 2023 | Magneto: A Foundation Transformer. Hongyu Wang, Shuming Ma, Shaohan Huang, Li Dong, Wenhui Wang, Zhiliang Peng, Yu Wu, Payal Bajaj, Saksham Singhal, Alon Benhaim, Barun Patra, Zhun Liu, Vishrav Chaudhary, Xia Song, Furu Wei |
| 2023 | Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models. Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, Zhou Zhao |
| 2023 | Margin-based Neural Network Watermarking. Byungjoo Kim, Suyoung Lee, Seanie Lee, Sooel Son, Sung Ju Hwang |
| 2023 | Margin-based sampling in high dimensions: When being active is less efficient than staying passive. Alexandru Tifrea, Jacob Clarysse, Fanny Yang |
| 2023 | Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations. David Wipf |
| 2023 | Markovian Gaussian Process Variational Autoencoders. Harrison Zhu, Carles Balsells Rodas, Yingzhen Li |
| 2023 | Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference. Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim |
| 2023 | Masked Trajectory Models for Prediction, Representation, and Control. Philipp Wu, Arjun Majumdar, Kevin Stone, Yixin Lin, Igor Mordatch, Pieter Abbeel, Aravind Rajeswaran |
| 2023 | Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning. Zhongzhi Yu, Yang Zhang, Kaizhi Qian, Cheng Wan, Yonggan Fu, Yongan Zhang, Yingyan Celine Lin |
| 2023 | Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels. Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron C. Courville, Alexandre Lacoste |
| 2023 | Matrix Estimation for Individual Fairness. Cindy Y. Zhang, Sarah Huiyi Cen, Devavrat Shah |
| 2023 | Maximal Initial Learning Rates in Deep ReLU Networks. Gaurav Iyer, Boris Hanin, David Rolnick |
| 2023 | Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming. Chunlin Sun, Shang Liu, Xiaocheng Li |
| 2023 | Measuring the Impact of Programming Language Distribution. Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta |
| 2023 | Mechanistic Mode Connectivity. Ekdeep Singh Lubana, Eric J. Bigelow, Robert P. Dick, David Scott Krueger, Hidenori Tanaka |
| 2023 | Memory-Based Dual Gaussian Processes for Sequential Learning. Paul Edmund Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan |
| 2023 | Memory-Based Meta-Learning on Non-Stationary Distributions. Tim Genewein, Grégoire Delétang, Anian Ruoss, Li Kevin Wenliang, Elliot Catt, Vincent Dutordoir, Jordi Grau-Moya, Laurent Orseau, Marcus Hutter, Joel Veness |
| 2023 | Men Also Do Laundry: Multi-Attribute Bias Amplification. Dora Zhao, Jerone Theodore Alexander Andrews, Alice Xiang |
| 2023 | Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe |
| 2023 | Meta Optimal Transport. Brandon Amos, Giulia Luise, Samuel Cohen, Ievgen Redko |
| 2023 | Meta-Learning the Inductive Bias of Simple Neural Circuits. Will Dorrell, Maria Yuffa, Peter E. Latham |
| 2023 | Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization. Jiwoo Son, Minsu Kim, Hyeonah Kim, Jinkyoo Park |
| 2023 | Meta-learning Parameterized Skills. Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris |
| 2023 | MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL. Fei Ni, Jianye Hao, Yao Mu, Yifu Yuan, Yan Zheng, Bin Wang, Zhixuan Liang |
| 2023 | MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks. Wenfang Sun, Yingjun Du, Xiantong Zhen, Fan Wang, Ling Wang, Cees G. M. Snoek |
| 2023 | Metagenomic Binning using Connectivity-constrained Variational Autoencoders. Andre Lamurias, Alessandro Tibo, Katja Hose, Mads Albertsen, Thomas Dyhre Nielsen |
| 2023 | MetricGAN-OKD: Multi-Metric Optimization of MetricGAN via Online Knowledge Distillation for Speech Enhancement. Wooseok Shin, Byung Hoon Lee, Jin Sob Kim, Hyun Joon Park, Sung Won Han |
| 2023 | Mimetic Initialization of Self-Attention Layers. Asher Trockman, J. Zico Kolter |
| 2023 | Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints. Alexandra Anna Lassota, Alexander Lindermayr, Nicole Megow, Jens Schlöter |
| 2023 | Minimax estimation of discontinuous optimal transport maps: The semi-discrete case. Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed |
| 2023 | Minimizing Trajectory Curvature of ODE-based Generative Models. Sangyun Lee, Beomsu Kim, Jong Chul Ye |
| 2023 | Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation. Li'ang Li, Yifei Duan, Guanghua Ji, Yongqiang Cai |
| 2023 | Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes. Marin Ballu, Quentin Berthet |
| 2023 | Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. Hongxin Wei, Huiping Zhuang, Renchunzi Xie, Lei Feng, Gang Niu, Bo An, Yixuan Li |
| 2023 | Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne |
| 2023 | Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning. Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman |
| 2023 | MixFlows: principled variational inference via mixed flows. Zuheng Xu, Naitong Chen, Trevor Campbell |
| 2023 | Mixing Predictions for Online Metric Algorithms. Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, Bertrand Simon |
| 2023 | Mixture Proportion Estimation Beyond Irreducibility. Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott |
| 2023 | Moccasin: Efficient Tensor Rematerialization for Neural Networks. Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina |
| 2023 | Modality-Agnostic Variational Compression of Implicit Neural Representations. Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh, Jaeho Lee, Jinwoo Shin |
| 2023 | Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization. Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz |
| 2023 | Model Transferability with Responsive Decision Subjects. Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu |
| 2023 | Model-Aware Contrastive Learning: Towards Escaping the Dilemmas. Zizheng Huang, Haoxing Chen, Ziqi Wen, Chao Zhang, Huaxiong Li, Bo Wang, Chunlin Chen |
| 2023 | Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning. Yihao Sun, Jiaji Zhang, Chengxing Jia, Haoxin Lin, Junyin Ye, Yang Yu |
| 2023 | Model-Free Robust Average-Reward Reinforcement Learning. Yue Wang, Alvaro Velasquez, George K. Atia, Ashley Prater-Bennette, Shaofeng Zou |
| 2023 | Model-agnostic Measure of Generalization Difficulty. Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete |
| 2023 | Model-based Offline Reinforcement Learning with Count-based Conservatism. Byeongchan Kim, Min-hwan Oh |
| 2023 | Model-based Reinforcement Learning with Scalable Composite Policy Gradient Estimators. Paavo Parmas, Takuma Seno, Yuma Aoki |
| 2023 | ModelDiff: A Framework for Comparing Learning Algorithms. Harshay Shah, Sung Min Park, Andrew Ilyas, Aleksander Madry |
| 2023 | Modeling Dynamic Environments with Scene Graph Memory. Andrey Kurenkov, Michael Lingelbach, Tanmay Agarwal, Emily Jin, Chengshu Li, Ruohan Zhang, Li Fei-Fei, Jiajun Wu, Silvio Savarese, Roberto Martín-Martín |
| 2023 | Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann |
| 2023 | Moderately Distributional Exploration for Domain Generalization. Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian |
| 2023 | MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma |
| 2023 | Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. Tao Sun, Qingsong Wang, Dongsheng Li, Bao Wang |
| 2023 | Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. Marco Cuturi, Michal Klein, Pierre Ablin |
| 2023 | MonoFlow: Rethinking Divergence GANs via the Perspective of Wasserstein Gradient Flows. Mingxuan Yi, Zhanxing Zhu, Song Liu |
| 2023 | MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Poses. Yang Fu, Ishan Misra, Xiaolong Wang |
| 2023 | Monotonic Location Attention for Length Generalization. Jishnu Ray Chowdhury, Cornelia Caragea |
| 2023 | Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney |
| 2023 | Motion Question Answering via Modular Motion Programs. Mark Endo, Joy Hsu, Jiaman Li, Jiajun Wu |
| 2023 | Mu Yong Cheng, Yu Zhang, Melvin Johnson, Wolfgang Macherey, Ankur Bapna |
| 2023 | Multi-Agent Best Arm Identification with Private Communications. Alexandre Rio, Merwan Barlier, Igor Colin, Marta Soare |
| 2023 | Multi-Agent Learning from Learners. Mine Melodi Caliskan, Francesco Chini, Setareh Maghsudi |
| 2023 | Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum |
| 2023 | Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning. Christopher A. Choquette-Choo, Hugh Brendan McMahan, J. Keith Rush, Abhradeep Guha Thakurta |
| 2023 | Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry. Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer, Youssef M. Marzouk |
| 2023 | Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces. Zhengdao Chen |
| 2023 | Multi-Modal Classifiers for Open-Vocabulary Object Detection. Prannay Kaul, Weidi Xie, Andrew Zisserman |
| 2023 | Multi-Objective GFlowNets. Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio |
| 2023 | Multi-Objective Population Based Training. Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman |
| 2023 | Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava |
| 2023 | Multi-Task Differential Privacy Under Distribution Skew. Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang |
| 2023 | Multi-Task Off-Policy Learning from Bandit Feedback. Joey Hong, Branislav Kveton, Manzil Zaheer, Sumeet Katariya, Mohammad Ghavamzadeh |
| 2023 | Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal. NareshKumar Gurulingan, Bahram Zonooz, Elahe Arani |
| 2023 | Multi-User Reinforcement Learning with Low Rank Rewards. Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain |
| 2023 | Multi-View Masked World Models for Visual Robotic Manipulation. Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel |
| 2023 | Multi-agent Online Scheduling: MMS Allocations for Indivisible Items. Shengwei Zhou, Rufan Bai, Xiaowei Wu |
| 2023 | Multi-channel Autobidding with Budget and ROI Constraints. Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni |
| 2023 | Multi-class Graph Clustering via Approximated Effective p-Resistance. Shota Saito, Mark Herbster |
| 2023 | Multi-task Hierarchical Adversarial Inverse Reinforcement Learning. Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal |
| 2023 | Multi-task Representation Learning for Pure Exploration in Linear Bandits. Yihan Du, Longbo Huang, Wen Sun |
| 2023 | MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks. Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu |
| 2023 | MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation. Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel |
| 2023 | MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal |
| 2023 | Multicalibration as Boosting for Regression. Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell |
| 2023 | Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation. Ronghao Dang, Lu Chen, Liuyi Wang, Zongtao He, Chengju Liu, Qijun Chen |
| 2023 | Multiplier Bootstrap-based Exploration. Runzhe Wan, Haoyu Wei, Branislav Kveton, Rui Song |
| 2023 | Multiply Robust Off-policy Evaluation and Learning under Truncation by Death. Jianing Chu, Shu Yang, Wenbin Lu |
| 2023 | Multisample Flow Matching: Straightening Flows with Minibatch Couplings. Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen |
| 2023 | Muse: Text-To-Image Generation via Masked Generative Transformers. Huiwen Chang, Han Zhang, Jarred Barber, Aaron Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Patrick Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan |
| 2023 | MyoDex: A Generalizable Prior for Dexterous Manipulation. Vittorio Caggiano, Sudeep Dasari, Vikash Kumar |
| 2023 | NA Zichuan Liu, Yuanyang Zhu, Chunlin Chen |
| 2023 | NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation. Tong Zhou, Yukui Luo, Shaolei Ren, Xiaolin Xu |
| 2023 | NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation. Jianfeng Wang, Daniela Massiceti, Xiaolin Hu, Vladimir Pavlovic, Thomas Lukasiewicz |
| 2023 | NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. Tianxin Wei, Zeming Guo, Yifan Chen, Jingrui He |
| 2023 | NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data. Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu |
| 2023 | Naive imputation implicitly regularizes high-dimensional linear models. Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet |
| 2023 | NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations. Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan Celine Lin |
| 2023 | Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. Kaiwen Wang, Nathan Kallus, Wen Sun |
| 2023 | Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime. Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar |
| 2023 | Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. Ilias Diakonikolas, Daniel Kane, Lisheng Ren |
| 2023 | Near-Optimal Quantum Coreset Construction Algorithms for Clustering. Yecheng Xue, Xiaoyu Chen, Tongyang Li, Shaofeng H.-C. Jiang |
| 2023 | Near-Optimal Φ-Regret Learning in Extensive-Form Games. Ioannis Anagnostides, Gabriele Farina, Tuomas Sandholm |
| 2023 | Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang |
| 2023 | Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2023 | Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu |
| 2023 | Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression. Junfan Li, Shizhong Liao |
| 2023 | Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests. Qixin Zhang, Wenbing Ye, Zaiyi Chen, Haoyuan Hu, Enhong Chen, Yu Yang |
| 2023 | Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas |
| 2023 | Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs. Steinar Laenen, Bogdan-Adrian Manghiuc, He Sun |
| 2023 | Nearly-tight Bounds for Deep Kernel Learning. Yifan Zhang, Min-Ling Zhang |
| 2023 | NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion. Jiatao Gu, Alex Trevithick, Kai-En Lin, Joshua M. Susskind, Christian Theobalt, Lingjie Liu, Ravi Ramamoorthi |
| 2023 | Nested Elimination: A Simple Algorithm for Best-Item Identification From Choice-Based Feedback. Junwen Yang, Yifan Feng |
| 2023 | Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan |
| 2023 | Network Effects in Performative Prediction Games. Xiaolu Wang, Chung-Yiu Yau, Hoi-To Wai |
| 2023 | Neural Algorithmic Reasoning with Causal Regularisation. Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic |
| 2023 | Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. Hien Dang, Tho Tran Huu, Stanley J. Osher, Hung Tran-The, Nhat Ho, Tan Minh Nguyen |
| 2023 | Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series. Abdul Fatir Ansari, Alvin Heng, Andre Lim, Harold Soh |
| 2023 | Neural Diffusion Processes. Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson |
| 2023 | Neural FIM for learning Fisher information metrics from point cloud data. Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong, Yanlei Zhang, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy |
| 2023 | Neural Inverse Operators for Solving PDE Inverse Problems. Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra |
| 2023 | Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data. Cheol Jun Cho, Edward F. Chang, Gopala Krishna Anumanchipalli |
| 2023 | Neural Markov Jump Processes. Patrick Seifner, Ramsés J. Sánchez |
| 2023 | Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods. Brian Irwin, Eldad Haber, Raviv Gal, Avi Ziv |
| 2023 | Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective. Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Andrej Risteski |
| 2023 | Neural Prediction Errors enable Analogical Visual Reasoning in Human Standard Intelligence Tests. Lingxiao Yang, Hongzhi You, Zonglei Zhen, Dahui Wang, Xiaohong Wan, Xiaohua Xie, Ru-Yuan Zhang |
| 2023 | Neural Status Registers. Lukas Faber, Roger Wattenhofer |
| 2023 | Neural Stochastic Differential Games for Time-series Analysis. Sungwoo Park, Byoungwoo Park, Moontae Lee, Changhee Lee |
| 2023 | Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels. Fabian Altekrüger, Johannes Hertrich, Gabriele Steidl |
| 2023 | Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks. T. Anderson Keller, Max Welling |
| 2023 | Neural networks trained with SGD learn distributions of increasing complexity. Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt |
| 2023 | Neural signature kernels as infinite-width-depth-limits of controlled ResNets. Nicola Muca Cirone, Maud Lemercier, Cristopher Salvi |
| 2023 | NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes. Chenbo Jiang, Jie Yang, Shwai He, Yu-Kun Lai, Lin Gao |
| 2023 | NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition. Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu |
| 2023 | Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal. Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, Stefano Teso |
| 2023 | Never mind the metrics - what about the uncertainty? Visualising binary confusion matrix metric distributions to put performance in perspective. David R. Lovell, Dimity Miller, Jaiden Capra, Andrew P. Bradley |
| 2023 | New metrics and search algorithms for weighted causal DAGs. Davin Choo, Kirankumar Shiragur |
| 2023 | No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation. Feilong Zhang, Xianming Liu, Shiyi Lin, Gang Wu, Xiong Zhou, Junjun Jiang, Xiangyang Ji |
| 2023 | Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks. Qiyu Kang, Kai Zhao, Yang Song, Sijie Wang, Wee Peng Tay |
| 2023 | Non-autoregressive Conditional Diffusion Models for Time Series Prediction. Lifeng Shen, James T. Kwok |
| 2023 | Non-stationary Reinforcement Learning under General Function Approximation. Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang |
| 2023 | Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think. Christian H. X. Ali Mehmeti-Göpel, Jan Disselhoff |
| 2023 | Nonlinear Causal Discovery with Latent Confounders. David Kaltenpoth, Jilles Vreeken |
| 2023 | Nonparametric Density Estimation under Distribution Drift. Alessio Mazzetto, Eli Upfal |
| 2023 | Nonparametric Extensions of Randomized Response for Private Confidence Sets. Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas |
| 2023 | Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows. Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin |
| 2023 | Nonparametric Iterative Machine Teaching. Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok |
| 2023 | Normalizing Flows for Interventional Density Estimation. Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel |
| 2023 | Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, Tianbao Yang |
| 2023 | Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits. Markus Bläser |
| 2023 | Nugget: Neural Agglomerative Embeddings of Text. Guanghui Qin, Benjamin Van Durme |
| 2023 | OCD: Learning to Overfit with Conditional Diffusion Models. Shahar Lutati, Lior Wolf |
| 2023 | ODS: Test-Time Adaptation in the Presence of Open-World Data Shift. Zhi Zhou, Lan-Zhe Guo, Lin-Han Jia, Dingchu Zhang, Yufeng Li |
| 2023 | OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models. Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang |
| 2023 | Off-Policy Average Reward Actor-Critic with Deterministic Policy Search. Naman Saxena, Subhojyoti Khastagir, Shishir Kolathaya, Shalabh Bhatnagar |
| 2023 | Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. Yuta Saito, Qingyang Ren, Thorsten Joachims |
| 2023 | Offline Learning in Markov Games with General Function Approximation. Yuheng Zhang, Yu Bai, Nan Jiang |
| 2023 | Offline Meta Reinforcement Learning with In-Distribution Online Adaptation. Jianhao Wang, Jin Zhang, Haozhe Jiang, Junyu Zhang, Liwei Wang, Chongjie Zhang |
| 2023 | Offline Reinforcement Learning with Closed-Form Policy Improvement Operators. Jiachen Li, Edwin Zhang, Ming Yin, Qinxun Bai, Yu-Xiang Wang, William Yang Wang |
| 2023 | Omnipredictors for Constrained Optimization. Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, Chutong Yang |
| 2023 | On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation. Maohao Shen, Yuheng Bu, Gregory W. Wornell |
| 2023 | On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization. Amir Joudaki, Hadi Daneshmand, Francis R. Bach |
| 2023 | On Computing Optimal Tree Ensembles. Christian Komusiewicz, Pascal Kunz, Frank Sommer, Manuel Sorge |
| 2023 | On Coresets for Clustering in Small Dimensional Euclidean spaces. Lingxiao Huang, Ruiyuan Huang, Zengfeng Huang, Xuan Wu |
| 2023 | On Data Manifolds Entailed by Structural Causal Models. Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Georgios Arvanitidis, Bernhard Schölkopf |
| 2023 | On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing. Sepanta Zeighami, Cyrus Shahabi |
| 2023 | On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network. Shijun Zhang, Jianfeng Lu, Hongkai Zhao |
| 2023 | On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances. Aritra Guha, Nhat Ho, XuanLong Nguyen |
| 2023 | On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs. Richard A. Watson, Hengrui Cai, Xinming An, Samuel A. McLean, Rui Song |
| 2023 | On Investigating the Conservative Property of Score-Based Generative Models. Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Chun-Yi Lee |
| 2023 | On Kinetic Optimal Probability Paths for Generative Models. Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman |
| 2023 | On Many-Actions Policy Gradient. Michal Nauman, Marek Cygan |
| 2023 | On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, Michael M. Bronstein |
| 2023 | On Penalty-based Bilevel Gradient Descent Method. Han Shen, Tianyi Chen |
| 2023 | On Pitfalls of Test-Time Adaptation. Hao Zhao, Yuejiang Liu, Alexandre Alahi, Tao Lin |
| 2023 | On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline. Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang |
| 2023 | On Preemption and Learning in Stochastic Scheduling. Nadav Merlis, Hugo Richard, Flore Sentenac, Corentin Odic, Mathieu Molina, Vianney Perchet |
| 2023 | On Provable Copyright Protection for Generative Models. Nikhil Vyas, Sham M. Kakade, Boaz Barak |
| 2023 | On Regularization and Inference with Label Constraints. Kaifu Wang, Hangfeng He, Tin D. Nguyen, Piyush Kumar, Dan Roth |
| 2023 | On Sampling with Approximate Transport Maps. Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié |
| 2023 | On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. Viktor Bengs, Eyke Hüllermeier, Willem Waegeman |
| 2023 | On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation. Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han |
| 2023 | On Uni-Modal Feature Learning in Supervised Multi-Modal Learning. Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Tianyuan Yuan, Yue Wang, Yang Yuan, Hang Zhao |
| 2023 | On User-Level Private Convex Optimization. Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang |
| 2023 | On the Complexity of Bayesian Generalization. Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu |
| 2023 | On the Connection Between MPNN and Graph Transformer. Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang |
| 2023 | On the Convergence Rate of Gaussianization with Random Rotations. Felix Draxler, Lars Kühmichel, Armand Rousselot, Jens Müller, Christoph Schnörr, Ullrich Köthe |
| 2023 | On the Convergence of Federated Averaging with Cyclic Client Participation. Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang |
| 2023 | On the Convergence of Gradient Flow on Multi-layer Linear Models. Hancheng Min, René Vidal, Enrique Mallada |
| 2023 | On the Convergence of SARSA with Linear Function Approximation. Shangtong Zhang, Remi Tachet des Combes, Romain Laroche |
| 2023 | On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters. Wonyeol Lee, Sejun Park, Alex Aiken |
| 2023 | On the Effectiveness of Offline RL for Dialogue Response Generation. Paloma Sodhi, Felix Wu, Ethan R. Elenberg, Kilian Q. Weinberger, Ryan McDonald |
| 2023 | On the Estimation of Gaussian Mixture Copula Models. Ashutosh Tewari |
| 2023 | On the Expressive Power of Geometric Graph Neural Networks. Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio |
| 2023 | On the Forward Invariance of Neural ODEs. Wei Xiao, Tsun-Hsuan Wang, Ramin M. Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, Daniela Rus |
| 2023 | On the Functional Similarity of Robust and Non-Robust Neural Representations. András Balogh, Márk Jelasity |
| 2023 | On the Generalization of Multi-modal Contrastive Learning. Qi Zhang, Yifei Wang, Yisen Wang |
| 2023 | On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization. Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal |
| 2023 | On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures. Xian Yu, Lei Ying |
| 2023 | On the Identifiability and Estimation of Causal Location-Scale Noise Models. Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx |
| 2023 | On the Impact of Algorithmic Recourse on Social Segregation. Ruijiang Gao, Himabindu Lakkaraju |
| 2023 | On the Impact of Knowledge Distillation for Model Interpretability. Hyeongrok Han, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon |
| 2023 | On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning. Hojoon Lee, Koanho Lee, Dongyoon Hwang, Hyunho Lee, Byungkun Lee, Jaegul Choo |
| 2023 | On the Initialization of Graph Neural Networks. Jiahang Li, Yakun Song, Xiang Song, David Wipf |
| 2023 | On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu |
| 2023 | On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs. Romain Laroche, Remi Tachet des Combes |
| 2023 | On the Optimality of Misspecified Kernel Ridge Regression. Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin |
| 2023 | On the Power of Foundation Models. Yang Yuan |
| 2023 | On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness. Haotian Ye, Xiaoyu Chen, Liwei Wang, Simon Shaolei Du |
| 2023 | On the Privacy-Robustness-Utility Trilemma in Distributed Learning. Youssef Allouah, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan |
| 2023 | On the Relationship Between Explanation and Prediction: A Causal View. Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim |
| 2023 | On the Robustness of Randomized Ensembles to Adversarial Perturbations. Hassan Dbouk, Naresh R. Shanbhag |
| 2023 | On the Robustness of Text Vectorizers. Rémi Catellier, Samuel Vaiter, Damien Garreau |
| 2023 | On the Role of Attention in Prompt-tuning. Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis |
| 2023 | On the Statistical Benefits of Temporal Difference Learning. David Cheikhi, Daniel Russo |
| 2023 | On the Stepwise Nature of Self-Supervised Learning. James B. Simon, Maksis Knutins, Liu Ziyin, Daniel Geisz, Abraham J. Fetterman, Joshua Albrecht |
| 2023 | On the Training Instability of Shuffling SGD with Batch Normalization. David Xing Wu, Chulhee Yun, Suvrit Sra |
| 2023 | On the Within-Group Fairness of Screening Classifiers. Nastaran Okati, Stratis Tsirtsis, Manuel Gomez Rodriguez |
| 2023 | On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm. Julien Aubert, Luc Lehéricy, Patricia Reynaud-Bouret |
| 2023 | One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale. Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu |
| 2023 | One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding. Daniel Severo, James Townsend, Ashish J. Khisti, Alireza Makhzani |
| 2023 | One-Shot Federated Conformal Prediction. Pierre Humbert, Batiste Le Bars, Aurélien Bellet, Sylvain Arlot |
| 2023 | One-Step Estimator for Permuted Sparse Recovery. Hang Zhang, Ping Li |
| 2023 | One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill. Sangwoo Shin, Daehee Lee, Minjong Yoo, Woo Kyung Kim, Honguk Woo |
| 2023 | One-sided Matrix Completion from Two Observations Per Row. Steven Cao, Percy Liang, Gregory Valiant |
| 2023 | One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training. Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi, Kentaro Ohno, Yasutoshi Ida |
| 2023 | Online Learning in Stackelberg Games with an Omniscient Follower. Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael I. Jordan |
| 2023 | Online Learning with Feedback Graphs: The True Shape of Regret. Tomás Kocák, Alexandra Carpentier |
| 2023 | Online Local Differential Private Quantile Inference via Self-normalization. Yi Liu, Qirui Hu, Lei Ding, Linglong Kong |
| 2023 | Online Mechanism Design for Information Acquisition. Federico Cacciamani, Matteo Castiglioni, Nicola Gatti |
| 2023 | Online Nonstochastic Control with Adversarial and Static Constraints. Xin Liu, Zixian Yang, Lei Ying |
| 2023 | Online Platt Scaling with Calibeating. Chirag Gupta, Aaditya Ramdas |
| 2023 | Online Prototype Alignment for Few-shot Policy Transfer. Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Yunkai Gao, Kaizhao Yuan, Ruizhi Chen, Siming Lan, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen |
| 2023 | Online Restless Bandits with Unobserved States. Bowen Jiang, Bo Jiang, Jian Li, Tao Lin, Xinbing Wang, Chenghu Zhou |
| 2023 | Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization. Zejia Weng, Xitong Yang, Ang Li, Zuxuan Wu, Yu-Gang Jiang |
| 2023 | Open-Vocabulary Universal Image Segmentation with MaskCLIP. Zheng Ding, Jieke Wang, Zhuowen Tu |
| 2023 | OpenFE: Automated Feature Generation with Expert-level Performance. Tianping Zhang, Zheyu Aqa Zhang, Zhiyuan Fan, Haoyan Luo, Fengyuan Liu, Qian Liu, Wei Cao, Li Jian |
| 2023 | Opponent-Limited Online Search for Imperfect Information Games. Weiming Liu, Haobo Fu, Qiang Fu, Wei Yang |
| 2023 | Optimal Arms Identification with Knapsacks. Shaoang Li, Lan Zhang, Yingqi Yu, Xiangyang Li |
| 2023 | Optimal Convergence Rates for Agnostic Nyström Kernel Learning. Jian Li, Yong Liu, Weiping Wang |
| 2023 | Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning. Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang |
| 2023 | Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs. Junkai Zhang, Weitong Zhang, Quanquan Gu |
| 2023 | Optimal LP Rounding and Linear-Time Approximation Algorithms for Clustering Edge-Colored Hypergraphs. Nate Veldt |
| 2023 | Optimal No-Regret Learning for One-Sided Lipschitz Functions. Paul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi Wang |
| 2023 | Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2023 | Optimal Rates and Efficient Algorithms for Online Bayesian Persuasion. Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Francesco Trovò, Nicola Gatti |
| 2023 | Optimal Sets and Solution Paths of ReLU Networks. Aaron Mishkin, Mert Pilanci |
| 2023 | Optimal Shrinkage for Distributed Second-Order Optimization. Fangzhao Zhang, Mert Pilanci |
| 2023 | Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion. Ashok Cutkosky, Harsh Mehta, Francesco Orabona |
| 2023 | Optimal randomized multilevel Monte Carlo for repeatedly nested expectations. Yasa Syed, Guanyang Wang |
| 2023 | Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits. Jongyeong Lee, Junya Honda, Chao-Kai Chiang, Masashi Sugiyama |
| 2023 | Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference. Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, François-Xavier Briol |
| 2023 | Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization. Sijia Chen, Wei-Wei Tu, Peng Zhao, Lijun Zhang |
| 2023 | Optimistic Planning by Regularized Dynamic Programming. Antoine Moulin, Gergely Neu |
| 2023 | Optimization for Amortized Inverse Problems. Tianci Liu, Tong Yang, Quan Zhang, Qi Lei |
| 2023 | Optimizing DDPM Sampling with Shortcut Fine-Tuning. Ying Fan, Kangwook Lee |
| 2023 | Optimizing Hyperparameters with Conformal Quantile Regression. David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau |
| 2023 | Optimizing Mode Connectivity for Class Incremental Learning. Haitao Wen, Haoyang Cheng, Heqian Qiu, Lanxiao Wang, Lili Pan, Hongliang Li |
| 2023 | Optimizing NOTEARS Objectives via Topological Swaps. Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar |
| 2023 | Optimizing the Collaboration Structure in Cross-Silo Federated Learning. Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He |
| 2023 | Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning. Matthias Gerstgrasser, David C. Parkes |
| 2023 | Orthogonality-Enforced Latent Space in Autoencoders: An Approach to Learning Disentangled Representations. Jaehoon Cha, Jeyan Thiyagalingam |
| 2023 | Oscillation-free Quantization for Low-bit Vision Transformers. Shih-Yang Liu, Zechun Liu, Kwang-Ting Cheng |
| 2023 | Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, Yi Chang |
| 2023 | Out-of-Domain Robustness via Targeted Augmentations. Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang |
| 2023 | Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation. Wenqing Zheng, S. P. Sharan, Ajay Kumar Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, Zhangyang Wang |
| 2023 | Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points. Ziye Ma, Igor Molybog, Javad Lavaei, Somayeh Sojoudi |
| 2023 | Overcoming Simplicity Bias in Deep Networks using a Feature Sieve. Rishabh Tiwari, Pradeep Shenoy |
| 2023 | PAC Generalization via Invariant Representations. Advait U. Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai |
| 2023 | PAC Prediction Sets for Large Language Models of Code. Adam Khakhar, Stephen Mell, Osbert Bastani |
| 2023 | PAC-Bayesian Generalization Bounds for Adversarial Generative Models. Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain |
| 2023 | PAC-Bayesian Offline Contextual Bandits With Guarantees. Otmane Sakhi, Pierre Alquier, Nicolas Chopin |
| 2023 | PAL: Program-aided Language Models. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig |
| 2023 | PASTA: Pessimistic Assortment Optimization. Juncheng Dong, Weibin Mo, Zhengling Qi, Cong Shi, Ethan X. Fang, Vahid Tarokh |
| 2023 | PCA-based Multi-Task Learning: a Random Matrix Approach. Malik Tiomoko, Romain Couillet, Frédéric Pascal |
| 2023 | PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola |
| 2023 | PFNs4BO: In-Context Learning for Bayesian Optimization. Samuel Müller, Matthias Feurer, Noah Hollmann, Frank Hutter |
| 2023 | PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu |
| 2023 | PLay: Parametrically Conditioned Layout Generation using Latent Diffusion. Chin-Yi Cheng, Forrest Huang, Gang Li, Yang Li |
| 2023 | POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models. Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng, Pengcheng He, Mingyuan Zhou |
| 2023 | PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient. Kaixin Wang, Daquan Zhou, Jiashi Feng, Shie Mannor |
| 2023 | PWSHAP: A Path-Wise Explanation Model for Targeted Variables. Lucile Ter-Minassian, Oscar Clivio, Karla DiazOrdaz, Robin J. Evans, Christopher C. Holmes |
| 2023 | PaLM-E: An Embodied Multimodal Language Model. Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence |
| 2023 | Paging with Succinct Predictions. Antonios Antoniadis, Joan Boyar, Marek Eliás, Lene Monrad Favrholdt, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon |
| 2023 | Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions. Boxiang Lyu, Zhe Feng, Zachary Robertson, Sanmi Koyejo |
| 2023 | Parallel Neurosymbolic Integration with Concordia. Jonathan Feldstein, Modestas Jurcius, Efthymia Tsamoura |
| 2023 | Parallel Online Clustering of Bandits via Hedonic Game. Xiaotong Cheng, Cheng Pan, Setareh Maghsudi |
| 2023 | Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal |
| 2023 | Parameter-Level Soft-Masking for Continual Learning. Tatsuya Konishi, Mori Kurokawa, Chihiro Ono, Zixuan Ke, Gyuhak Kim, Bing Liu |
| 2023 | Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models. Nikolaos Dimitriadis, Pascal Frossard, François Fleuret |
| 2023 | Pareto Regret Analyses in Multi-objective Multi-armed Bandit. Mengfan Xu, Diego Klabjan |
| 2023 | Partial Optimality in Cubic Correlation Clustering. David Stein, Silvia Di Gregorio, Bjoern Andres |
| 2023 | Partially Observable Multi-agent RL with (Quasi-)Efficiency: The Blessing of Information Sharing. Xiangyu Liu, Kaiqing Zhang |
| 2023 | Patch-level Contrastive Learning via Positional Query for Visual Pre-training. Shaofeng Zhang, Qiang Zhou, Zhibin Wang, Fan Wang, Junchi Yan |
| 2023 | Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen |
| 2023 | Path Neural Networks: Expressive and Accurate Graph Neural Networks. Gaspard Michel, Giannis Nikolentzos, Johannes F. Lutzeyer, Michalis Vazirgiannis |
| 2023 | Performative Recommendation: Diversifying Content via Strategic Incentives. Itay Eilat, Nir Rosenfeld |
| 2023 | Performative Reinforcement Learning. Debmalya Mandal, Stelios Triantafyllou, Goran Radanovic |
| 2023 | Personalized Federated Learning under Mixture of Distributions. Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng |
| 2023 | Personalized Federated Learning with Inferred Collaboration Graphs. Rui Ye, Zhenyang Ni, Fangzhao Wu, Siheng Chen, Yanfeng Wang |
| 2023 | Personalized Subgraph Federated Learning. Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang |
| 2023 | Perturbation Analysis of Neural Collapse. Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed |
| 2023 | Phase Transitions in the Detection of Correlated Databases. Dor Elimelech, Wasim Huleihel |
| 2023 | Phase-aware Adversarial Defense for Improving Adversarial Robustness. Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu |
| 2023 | Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding. Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova |
| 2023 | PixelAsParam: A Gradient View on Diffusion Sampling with Guidance. AnhDung Dinh, Daochang Liu, Chang Xu |
| 2023 | Poisoning Generative Replay in Continual Learning to Promote Forgetting. Siteng Kang, Zhan Shi, Xinhua Zhang |
| 2023 | Poisoning Language Models During Instruction Tuning. Alexander Wan, Eric Wallace, Sheng Shen, Dan Klein |
| 2023 | Polarity Is All You Need to Learn and Transfer Faster. Qingyang Wang, Michael Alan Powell, Eric W. Bridgeford, Ali Geisa, Joshua T. Vogelstein |
| 2023 | Policy Contrastive Imitation Learning. Jialei Huang, Zhao-Heng Yin, Yingdong Hu, Yang Gao |
| 2023 | Policy Gradient in Robust MDPs with Global Convergence Guarantee. Qiuhao Wang, Chin Pang Ho, Marek Petrik |
| 2023 | Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games. Batuhan Yardim, Semih Cayci, Matthieu Geist, Niao He |
| 2023 | Policy Regularization with Dataset Constraint for Offline Reinforcement Learning. Yuhang Ran, Yi-Chen Li, Fuxiang Zhang, Zongzhang Zhang, Yang Yu |
| 2023 | Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision. Arturs Berzins |
| 2023 | Polynomial Preconditioning for Gradient Methods. Nikita Doikov, Anton Rodomanov |
| 2023 | Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. Jamil Arbas, Hassan Ashtiani, Christopher Liaw |
| 2023 | Posterior Sampling for Deep Reinforcement Learning. Remo Sasso, Michelangelo Conserva, Paulo E. Rauber |
| 2023 | Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner |
| 2023 | Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute. Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Joshua Ainslie, Sumit Sanghai, Fei Sha, William W. Cohen |
| 2023 | Pre-training for Speech Translation: CTC Meets Optimal Transport. Phuong-Hang Le, Hongyu Gong, Changhan Wang, Juan Pino, Benjamin Lecouteux, Didier Schwab |
| 2023 | PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search. Haibin Wang, Ce Ge, Hesen Chen, Xiuyu Sun |
| 2023 | Predictable MDP Abstraction for Unsupervised Model-Based RL. Seohong Park, Sergey Levine |
| 2023 | Predicting Ordinary Differential Equations with Transformers. Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus |
| 2023 | Predicting Rare Events by Shrinking Towards Proportional Odds. Gregory Faletto, Jacob Bien |
| 2023 | Predictive Flows for Faster Ford-Fulkerson. Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang |
| 2023 | Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning. Jaehyung Kim, Jinwoo Shin, Dongyeop Kang |
| 2023 | Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. Chawin Sitawarin, Florian Tramèr, Nicholas Carlini |
| 2023 | Pretraining Language Models with Human Preferences. Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, Ethan Perez |
| 2023 | Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk. David Simchi-Levi, Chonghuan Wang |
| 2023 | Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems. Atsushi Nitanda, Kazusato Oko, Denny Wu, Nobuhito Takenouchi, Taiji Suzuki |
| 2023 | Principled Acceleration of Iterative Numerical Methods Using Machine Learning. Sohei Arisaka, Qianxiao Li |
| 2023 | Principled Offline RL in the Presence of Rich Exogenous Information. Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Rajiv Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, John Langford |
| 2023 | Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons. Banghua Zhu, Michael I. Jordan, Jiantao Jiao |
| 2023 | Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. Chuan Guo, Kamalika Chaudhuri, Pierre Stock, Michael G. Rabbat |
| 2023 | Private Federated Learning with Autotuned Compression. Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz, Sewoong Oh |
| 2023 | Private Statistical Estimation of Many Quantiles. Clément Lalanne, Aurélien Garivier, Rémi Gribonval |
| 2023 | Probabilistic Attention-to-Influence Neural Models for Event Sequences. Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin P. Bennett |
| 2023 | Probabilistic Categorical Adversarial Attack and Adversarial Training. Han Xu, Pengfei He, Jie Ren, Yuxuan Wan, Zitao Liu, Hui Liu, Jiliang Tang |
| 2023 | Probabilistic Concept Bottleneck Models. Eunji Kim, Dahuin Jung, Sangha Park, Siwon Kim, Sungroh Yoon |
| 2023 | Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs. Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh |
| 2023 | Probabilistic Imputation for Time-series Classification with Missing Data. Seunghyun Kim, Hyunsu Kim, EungGu Yun, Hwangrae Lee, Jaehun Lee, Juho Lee |
| 2023 | Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models. Alexander Lin, Bahareh Tolooshams, Yves F. Atchadé, Demba E. Ba |
| 2023 | Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits. Yunlong Hou, Vincent Y. F. Tan, Zixin Zhong |
| 2023 | Progressive Purification for Instance-Dependent Partial Label Learning. Ning Xu, Biao Liu, Jiaqi Lv, Congyu Qiao, Xin Geng |
| 2023 | Projected Tensor Power Method for Hypergraph Community Recovery. Jinxin Wang, Yuen-Man Pun, Xiaolu Wang, Peng Wang, Anthony Man-Cho So |
| 2023 | Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning. Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu |
| 2023 | PromptBoosting: Black-Box Text Classification with Ten Forward Passes. Bairu Hou, Joe O'Connor, Jacob Andreas, Shiyu Chang, Yang Zhang |
| 2023 | Prompting Large Language Model for Machine Translation: A Case Study. Biao Zhang, Barry Haddow, Alexandra Birch |
| 2023 | Propensity Matters: Measuring and Enhancing Balancing for Recommendation. Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu, Peng Cui |
| 2023 | Proper Losses for Discrete Generative Models. Dhamma Kimpara, Rafael M. Frongillo, Bo Waggoner |
| 2023 | Proper Scoring Rules for Survival Analysis. Hiroki Yanagisawa |
| 2023 | Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist. Niclas Boehmer, Piotr Faliszewski, Sonja Kraiczy |
| 2023 | ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts. Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang |
| 2023 | Protecting Language Generation Models via Invisible Watermarking. Xuandong Zhao, Yu-Xiang Wang, Lei Li |
| 2023 | Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning. Nader Asadi, MohammadReza Davari, Sudhir P. Mudur, Rahaf Aljundi, Eugene Belilovsky |
| 2023 | Prototype-oriented unsupervised anomaly detection for multivariate time series. Yuxin Li, Wenchao Chen, Bo Chen, Dongsheng Wang, Long Tian, Mingyuan Zhou |
| 2023 | Provable Benefit of Mixup for Finding Optimal Decision Boundaries. Junsoo Oh, Chulhee Yun |
| 2023 | Provable Data Subset Selection For Efficient Neural Networks Training. Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman |
| 2023 | Provable Dynamic Fusion for Low-Quality Multimodal Data. Qingyang Zhang, Haitao Wu, Changqing Zhang, Qinghua Hu, Huazhu Fu, Joey Tianyi Zhou, Xi Peng |
| 2023 | Provable Multi-instance Deep AUC Maximization with Stochastic Pooling. Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang |
| 2023 | Provable Reset-free Reinforcement Learning by No-Regret Reduction. Hoai-An Nguyen, Ching-An Cheng |
| 2023 | Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka |
| 2023 | Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang |
| 2023 | Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang |
| 2023 | Provably Invariant Learning without Domain Information. Xiaoyu Tan, Lin Yong, Shengyu Zhu, Chao Qu, Xihe Qiu, Yinghui Xu, Peng Cui, Yuan Qi |
| 2023 | Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge |
| 2023 | Provably Learning Object-Centric Representations. Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel |
| 2023 | Provably and Practically Efficient Neural Contextual Bandits. Sudeep Salgia |
| 2023 | Proximal Causal Learning of Conditional Average Treatment Effects. Erik Sverdrup, Yifan Cui |
| 2023 | Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint. Zhanfeng Mo, Haosen Shi, Sinno Jialin Pan |
| 2023 | Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling. Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, Oskar van der Wal |
| 2023 | Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic |
| 2023 | Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL. Taku Yamagata, Ahmed Khalil, Raúl Santos-Rodríguez |
| 2023 | QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark. Xudong Lu, Kaisen Pan, Ge Yan, Jiaming Shan, Wenjie Wu, Junchi Yan |
| 2023 | QASA: Advanced Question Answering on Scientific Articles. Yoonjoo Lee, Kyungjae Lee, Sunghyun Park, Dasol Hwang, Jaehyeon Kim, Hong-in Lee, Moontae Lee |
| 2023 | Quantifying Human Priors over Social and Navigation Networks. Gecia Bravo Hermsdorff |
| 2023 | Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs. Lirong Wu, Haitao Lin, Yufei Huang, Stan Z. Li |
| 2023 | Quantifying the Variability Collapse of Neural Networks. Jing Xu, Haoxiong Liu |
| 2023 | Quantile Credit Assignment. Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos |
| 2023 | Quantitative Universal Approximation Bounds for Deep Belief Networks. Julian Sieber, Johann Gehringer |
| 2023 | Quantized Distributed Training of Large Models with Convergence Guarantees. Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh |
| 2023 | Quantum 3D Graph Learning with Applications to Molecule Embedding. Ge Yan, Huaijin Wu, Junchi Yan |
| 2023 | Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions. Chenyi Zhang, Tongyang Li |
| 2023 | Quantum Policy Gradient Algorithm with Optimized Action Decoding. Nico Meyer, Daniel D. Scherer, Axel Plinge, Christopher Mutschler, Michael J. Hartmann |
| 2023 | Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation. Hayata Yamasaki, Sathyawageeswar Subramanian, Satoshi Hayakawa, Sho Sonoda |
| 2023 | Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. Adam Bouland, Yosheb M. Getachew, Yujia Jin, Aaron Sidford, Kevin Tian |
| 2023 | QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms. Wenjie Wu, Ge Yan, Xudong Lu, Kaisen Pan, Junchi Yan |
| 2023 | R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. Daniel D. Johnson, Daniel Tarlow, Christian Walder |
| 2023 | RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution. Pengyi Li, Jianye Hao, Hongyao Tang, Yan Zheng, Xian Fu |
| 2023 | RGE: A Repulsive Graph Rectification for Node Classification via Influence. Jaeyun Song, Sungyub Kim, Eunho Yang |
| 2023 | RLEG: Vision-Language Representation Learning with Diffusion-based Embedding Generation. Liming Zhao, Kecheng Zheng, Yun Zheng, Deli Zhao, Jingren Zhou |
| 2023 | RLSbench: Domain Adaptation Under Relaxed Label Shift. Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton |
| 2023 | RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents. Rafael Rodríguez-Sánchez, Benjamin Adin Spiegel, Jennifer Wang, Roma Patel, Stefanie Tellex, George Konidaris |
| 2023 | RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations. Zirui Liu, Shengyuan Chen, Kaixiong Zhou, Daochen Zha, Xiao Huang, Xia Hu |
| 2023 | Raising the Cost of Malicious AI-Powered Image Editing. Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry |
| 2023 | Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. Yishay Mansour, Richard Nock, Robert C. Williamson |
| 2023 | Random Grid Neural Processes for Parametric Partial Differential Equations. Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz |
| 2023 | Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux |
| 2023 | Random Shuffle Transformer for Image Restoration. Jie Xiao, Xueyang Fu, Man Zhou, Hongjian Liu, Zheng-Jun Zha |
| 2023 | Random Teachers are Good Teachers. Felix Sarnthein, Gregor Bachmann, Sotiris Anagnostidis, Thomas Hofmann |
| 2023 | Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds. Shion Takeno, Yu Inatsu, Masayuki Karasuyama |
| 2023 | Randomized Schur Complement Views for Graph Contrastive Learning. Vignesh Kothapalli |
| 2023 | RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank. Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun |
| 2023 | ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval. Kexun Zhang, Xianjun Yang, William Yang Wang, Lei Li |
| 2023 | ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs. Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy |
| 2023 | Reachability-Aware Laplacian Representation in Reinforcement Learning. Kaixin Wang, Kuangqi Zhou, Jiashi Feng, Bryan Hooi, Xinchao Wang |
| 2023 | Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality. Guy Ohayon, Theo Joseph Adrai, Michael Elad, Tomer Michaeli |
| 2023 | Recasting Self-Attention with Holographic Reduced Representations. Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt |
| 2023 | Reconstructive Neuron Pruning for Backdoor Defense. Yige Li, Xixiang Lyu, Xingjun Ma, Nodens Koren, Lingjuan Lyu, Bo Li, Yu-Gang Jiang |
| 2023 | Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing. Hyeonsu Jeong, Hye Won Chung |
| 2023 | Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework. Arman Rahbar, Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt P. Dubhashi, Hamid Krim |
| 2023 | Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl |
| 2023 | Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs. Saro Passaro, C. Lawrence Zitnick |
| 2023 | Refined Regret for Adversarial MDPs with Linear Function Approximation. Yan Dai, Haipeng Luo, Chen-Yu Wei, Julian Zimmert |
| 2023 | Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models. Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, Il-Chul Moon |
| 2023 | Reflected Diffusion Models. Aaron Lou, Stefano Ermon |
| 2023 | Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts. Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados |
| 2023 | Regression with Label Permutation in Generalized Linear Model. Guanhua Fang, Ping Li |
| 2023 | Regression with Sensor Data Containing Incomplete Observations. Takayuki Katsuki, Takayuki Osogami |
| 2023 | Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents. Wenhao Xu, Xuefeng Gao, Xuedong He |
| 2023 | Regret Minimization and Convergence to Equilibria in General-sum Markov Games. Liad Erez, Tal Lancewicki, Uri Sherman, Tomer Koren, Yishay Mansour |
| 2023 | Regret-Minimizing Double Oracle for Extensive-Form Games. Xiaohang Tang, Le Cong Dinh, Stephen Marcus McAleer, Yaodong Yang |
| 2023 | Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo |
| 2023 | Regularization-free Diffeomorphic Temporal Alignment Nets. Ron Shapira Weber, Oren Freifeld |
| 2023 | Regularizing Towards Soft Equivariance Under Mixed Symmetries. Hyunsu Kim, Hyungi Lee, Hongseok Yang, Juho Lee |
| 2023 | Reinforcement Learning Can Be More Efficient with Multiple Rewards. Christoph Dann, Yishay Mansour, Mehryar Mohri |
| 2023 | Reinforcement Learning from Passive Data via Latent Intentions. Dibya Ghosh, Chethan Anand Bhateja, Sergey Levine |
| 2023 | Reinforcement Learning in Low-rank MDPs with Density Features. Audrey Huang, Jinglin Chen, Nan Jiang |
| 2023 | Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. Anas Barakat, Ilyas Fatkhullin, Niao He |
| 2023 | Reinforcement Learning with History Dependent Dynamic Contexts. Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier |
| 2023 | Relevant Walk Search for Explaining Graph Neural Networks. Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima |
| 2023 | Reliable Measures of Spread in High Dimensional Latent Spaces. Anna C. Marbut, Katy McKinney-Bock, Travis J. Wheeler |
| 2023 | Reparameterized Policy Learning for Multimodal Trajectory Optimization. Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li, Chuang Gan, Hao Su |
| 2023 | Repository-Level Prompt Generation for Large Language Models of Code. Disha Shrivastava, Hugo Larochelle, Daniel Tarlow |
| 2023 | Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL. Zakaria Mhammedi, Dylan J. Foster, Alexander Rakhlin |
| 2023 | Representation-Driven Reinforcement Learning. Ofir Nabati, Guy Tennenholtz, Shie Mannor |
| 2023 | Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa |
| 2023 | Representer Point Selection for Explaining Regularized High-dimensional Models. Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar |
| 2023 | Reprogramming Pretrained Language Models for Antibody Sequence Infilling. Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das |
| 2023 | Restoration based Generative Models. Jaemoo Choi, Yesom Park, Myungjoo Kang |
| 2023 | Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. Sitan Chen, Giannis Daras, Alex Dimakis |
| 2023 | Resurrecting Recurrent Neural Networks for Long Sequences. Antonio Orvieto, Samuel L. Smith, Albert Gu, Anushan Fernando, Çaglar Gülçehre, Razvan Pascanu, Soham De |
| 2023 | Rethink DARTS Search Space and Renovate a New Benchmark. Jiuling Zhang, Zhiming Ding |
| 2023 | Rethinking Backdoor Attacks. Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry |
| 2023 | Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching. Fang Wu, Siyuan Li, Xurui Jin, Yinghui Jiang, Dragomir Radev, Zhangming Niu, Stan Z. Li |
| 2023 | Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues. Jiaxuan Chen, Yu Qi, Gang Pan |
| 2023 | Rethinking Warm-Starts with Predictions: Learning Predictions Close to Sets of Optimal Solutions for Faster L-/L Shinsaku Sakaue, Taihei Oki |
| 2023 | Rethinking Weak Supervision in Helping Contrastive Learning. Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang |
| 2023 | Retrieval-Augmented Multimodal Language Modeling. Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Richard James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih |
| 2023 | Retrosynthetic Planning with Dual Value Networks. Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu |
| 2023 | Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. Shahine Bouabid, Jake Fawkes, Dino Sejdinovic |
| 2023 | Revisiting Bellman Errors for Offline Model Selection. Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael Rene Kosorok |
| 2023 | Revisiting Data-Free Knowledge Distillation with Poisoned Teachers. Junyuan Hong, Yi Zeng, Shuyang Yu, Lingjuan Lyu, Ruoxi Jia, Jiayu Zhou |
| 2023 | Revisiting Discriminative vs. Generative Classifiers: Theory and Implications. Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu |
| 2023 | Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization. Yun-Hsuan Lien, Ping-Chun Hsieh, Yu-Shuen Wang |
| 2023 | Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich |
| 2023 | Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature. Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley J. Osher, Tan Minh Nguyen |
| 2023 | Revisiting Pseudo-Label for Single-Positive Multi-Label Learning. Biao Liu, Ning Xu, Jiaqi Lv, Xin Geng |
| 2023 | Revisiting Sampling for Combinatorial Optimization. Haoran Sun, Katayoon Goshvadi, Azade Nova, Dale Schuurmans, Hanjun Dai |
| 2023 | Revisiting Simple Regret: Fast Rates for Returning a Good Arm. Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun |
| 2023 | Revisiting Structured Variational Autoencoders. Yixiu Zhao, Scott W. Linderman |
| 2023 | Revisiting Weighted Aggregation in Federated Learning with Neural Networks. Zexi Li, Tao Lin, Xinyi Shang, Chao Wu |
| 2023 | Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang |
| 2023 | Reward-Mixing MDPs with Few Latent Contexts are Learnable. Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor |
| 2023 | Rigid Body Flows for Sampling Molecular Crystal Structures. Jonas Köhler, Michele Invernizzi, Pim de Haan, Frank Noé |
| 2023 | Robust Budget Pacing with a Single Sample. Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang |
| 2023 | Robust Camera Pose Refinement for Multi-Resolution Hash Encoding. Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J. Kim, Jin-Hwa Kim |
| 2023 | Robust Collaborative Learning with Linear Gradient Overhead. Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan |
| 2023 | Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues. Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stéphan Clémençon |
| 2023 | Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta |
| 2023 | Robust Explanation for Free or At the Cost of Faithfulness. Zeren Tan, Yang Tian |
| 2023 | Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning. Junghoon Kim, Taejoon Kim, David J. Love, Christopher G. Brinton |
| 2023 | Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks. Louis Béthune, Paul Novello, Guillaume Coiffier, Thibaut Boissin, Mathieu Serrurier, Quentin Vincenot, Andres Troya-Galvis |
| 2023 | Robust Perception through Equivariance. Chengzhi Mao, Lingyu Zhang, Abhishek Vaibhav Joshi, Junfeng Yang, Hao Wang, Carl Vondrick |
| 2023 | Robust Satisficing MDPs. Haolin Ruan, Siyu Zhou, Zhi Chen, Chin Pang Ho |
| 2023 | Robust Situational Reinforcement Learning in Face of Context Disturbances. Jinpeng Zhang, Yufeng Zheng, Chuheng Zhang, Li Zhao, Lei Song, Yuan Zhou, Jiang Bian |
| 2023 | Robust Speech Recognition via Large-Scale Weak Supervision. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever |
| 2023 | Robust Subtask Learning for Compositional Generalization. Kishor Jothimurugan, Steve Hsu, Osbert Bastani, Rajeev Alur |
| 2023 | Robust Weak Supervision with Variational Auto-Encoders. Francesco Tonolini, Nikolaos Aletras, Yunlong Jiao, Gabriella Kazai |
| 2023 | Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights? Ruisi Cai, Zhenyu Zhang, Zhangyang Wang |
| 2023 | Robust and Scalable Bayesian Online Changepoint Detection. Matías Altamirano, François-Xavier Briol, Jeremias Knoblauch |
| 2023 | Robust and private stochastic linear bandits. Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni |
| 2023 | Robustly Learning a Single Neuron via Sharpness. Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas |
| 2023 | Robustness in Multimodal Learning under Train-Test Modality Mismatch. Brandon McKinzie, Vaishaal Shankar, Joseph Yitan Cheng, Yinfei Yang, Jonathon Shlens, Alexander T. Toshev |
| 2023 | Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch. Xunyi Zhao, Théotime Le Hellard, Lionel Eyraud-Dubois, Julia Gusak, Olivier Beaumont |
| 2023 | Rotation and Translation Invariant Representation Learning with Implicit Neural Representations. Sehyun Kwon, Joo Young Choi, Ernest K. Ryu |
| 2023 | Run-off Election: Improved Provable Defense against Data Poisoning Attacks. Keivan Rezaei, Kiarash Banihashem, Atoosa Malemir Chegini, Soheil Feizi |
| 2023 | SAAL: Sharpness-Aware Active Learning. Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, Il-Chul Moon |
| 2023 | SAM operates far from home: eigenvalue regularization as a dynamical phenomenon. Atish Agarwala, Yann N. Dauphin |
| 2023 | SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation. Shikun Sun, Longhui Wei, Junliang Xing, Jia Jia, Qi Tian |
| 2023 | SE(3) diffusion model with application to protein backbone generation. Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, Tommi S. Jaakkola |
| 2023 | SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning. Junran Wu, Xueyuan Chen, Bowen Shi, Shangzhe Li, Ke Xu |
| 2023 | SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance. Amit Attia, Tomer Koren |
| 2023 | SGD with Large Step Sizes Learns Sparse Features. Maksym Andriushchenko, Aditya Vardhan Varre, Loucas Pillaud-Vivien, Nicolas Flammarion |
| 2023 | SLAMB: Accelerated Large Batch Training with Sparse Communication. Hang Xu, Wenxuan Zhang, Jiawei Fei, Yuzhe Wu, Tingwen Xie, Jun Huang, Yuchen Xie, Mohamed Elhoseiny, Panos Kalnis |
| 2023 | SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process. Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang, Chao Zhang, Tuo Zhao, Hongyuan Zha |
| 2023 | SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning. Dongseok Shim, Seungjae Lee, H. Jin Kim |
| 2023 | SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series. Iris A. M. Huijben, Arthur Andreas Nijdam, Sebastiaan Overeem, Merel M. van Gilst, Ruud van Sloun |
| 2023 | SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. Tanguy Marchand, Regis Loeb, Ulysse Marteau-Ferey, Jean Ogier du Terrail, Arthur Pignet |
| 2023 | STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha |
| 2023 | STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh |
| 2023 | SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov |
| 2023 | Safe Offline Reinforcement Learning with Real-Time Budget Constraints. Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang |
| 2023 | Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models. Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma |
| 2023 | Sample Complexity Bounds for Learning High-dimensional Simplices in Noisy Regimes. Seyed Amir Hossein Saberi, Amir Najafi, Abolfazl S. Motahari, Babak H. Khalaj |
| 2023 | Sample Complexity of Probability Divergences under Group Symmetry. Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu |
| 2023 | Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation. Ilan Naiman, Nimrod Berman, Omri Azencot |
| 2023 | Sampling-Based Accuracy Testing of Posterior Estimators for General Inference. Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault Levasseur |
| 2023 | Sampling-based Nyström Approximation and Kernel Quadrature. Satoshi Hayakawa, Harald Oberhauser, Terry J. Lyons |
| 2023 | Scalable Adaptive Computation for Iterative Generation. Allan Jabri, David J. Fleet, Ting Chen |
| 2023 | Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation. Siddharth Nayak, Kenneth Choi, Wenqi Ding, Sydney Dolan, Karthik Gopalakrishnan, Hamsa Balakrishnan |
| 2023 | Scalable Safe Policy Improvement via Monte Carlo Tree Search. Alberto Castellini, Federico Bianchi, Edoardo Zorzi, Thiago D. Simão, Alessandro Farinelli, Matthijs T. J. Spaan |
| 2023 | Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation. Jeffrey Willette, Seanie Lee, Bruno Andreis, Kenji Kawaguchi, Juho Lee, Sung Ju Hwang |
| 2023 | Scaling Laws for Generative Mixed-Modal Language Models. Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, Luke Zettlemoyer |
| 2023 | Scaling Laws for Multilingual Neural Machine Translation. Patrick Fernandes, Behrooz Ghorbani, Xavier Garcia, Markus Freitag, Orhan Firat |
| 2023 | Scaling Laws for Reward Model Overoptimization. Leo Gao, John Schulman, Jacob Hilton |
| 2023 | Scaling Spherical CNNs. Carlos Esteves, Jean-Jacques E. Slotine, Ameesh Makadia |
| 2023 | Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh |
| 2023 | Scaling Vision Transformers to 22 Billion Parameters. Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby |
| 2023 | Scaling of Class-wise Training Losses for Post-hoc Calibration. Seungjin Jung, Seungmo Seo, Yonghyun Jeong, Jongwon Choi |
| 2023 | Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang |
| 2023 | SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models. Shenghua Wan, Yucen Wang, Minghao Shao, Ruying Chen, De-Chuan Zhan |
| 2023 | Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner |
| 2023 | Second-Order Optimization with Lazy Hessians. Nikita Doikov, El Mahdi Chayti, Martin Jaggi |
| 2023 | Second-order regression models exhibit progressive sharpening to the edge of stability. Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington |
| 2023 | Secure Federated Correlation Test and Entropy Estimation. Qi Pang, Lun Wang, Shuai Wang, Wenting Zheng, Dawn Song |
| 2023 | SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching. Liren Yu, Jiaming Xu, Xiaojun Lin |
| 2023 | SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation. Huaishao Luo, Junwei Bao, Youzheng Wu, Xiaodong He, Tianrui Li |
| 2023 | Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction. Khai Nguyen, Dang Nguyen, Nhat Ho |
| 2023 | Self-Interpretable Time Series Prediction with Counterfactual Explanations. Jingquan Yan, Hao Wang |
| 2023 | Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains. Vishwaraj Doshi, Jie Hu, Do Young Eun |
| 2023 | Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations. Weiwei Lin, Chenhang He, Man-Wai Mak, Youzhi Tu |
| 2023 | Self-supervised learning of Split Invariant Equivariant representations. Quentin Garrido, Laurent Najman, Yann LeCun |
| 2023 | SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification. Pranjal Aggarwal, Ameet Deshpande, Karthik R. Narasimhan |
| 2023 | Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees. Ioannis Panageas, Stratis Skoulakis, Luca Viano, Xiao Wang, Volkan Cevher |
| 2023 | Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows. Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov |
| 2023 | Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm. Adrien Vacher, François-Xavier Vialard |
| 2023 | Semi-Offline Reinforcement Learning for Optimized Text Generation. Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan |
| 2023 | Semi-Parametric Contextual Pricing Algorithm using Cox Proportional Hazards Model. Young-Geun Choi, Gi-Soo Kim, Yunseo Choi, Wooseong Cho, Myunghee Cho Paik, Min-hwan Oh |
| 2023 | Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover |
| 2023 | Semiparametrically Efficient Off-Policy Evaluation in Linear Markov Decision Processes. Chuhan Xie, Wenhao Yang, Zhihua Zhang |
| 2023 | Sequence Modeling with Multiresolution Convolutional Memory. Jiaxin Shi, Ke Alexander Wang, Emily B. Fox |
| 2023 | Sequential Changepoint Detection via Backward Confidence Sequences. Shubhanshu Shekhar, Aaditya Ramdas |
| 2023 | Sequential Counterfactual Risk Minimization. Houssam Zenati, Eustache Diemert, Matthieu Martin, Julien Mairal, Pierre Gaillard |
| 2023 | Sequential Kernelized Independence Testing. Aleksandr Podkopaev, Patrick Blöbaum, Shiva Prasad Kasiviswanathan, Aaditya Ramdas |
| 2023 | Sequential Monte Carlo Learning for Time Series Structure Discovery. Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka |
| 2023 | Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. Aniruddh Raghu, Payal Chandak, Ridwan Alam, John V. Guttag, Collin M. Stultz |
| 2023 | Sequential Predictive Conformal Inference for Time Series. Chen Xu, Yao Xie |
| 2023 | Sequential Strategic Screening. Lee Cohen, Saeed Sharifi-Malvajerdi, Kevin Stangl, Ali Vakilian, Juba Ziani |
| 2023 | Sequential Underspecified Instrument Selection for Cause-Effect Estimation. Elisabeth Ailer, Jason S. Hartford, Niki Kilbertus |
| 2023 | Set-membership Belief State-based Reinforcement Learning for POMDPs. Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang |
| 2023 | Settling the Reward Hypothesis. Michael Bowling, John D. Martin, David Abel, Will Dabney |
| 2023 | Shape-Guided Dual-Memory Learning for 3D Anomaly Detection. Yu-Min Chu, Chieh Liu, Ting-I Hsieh, Hwann-Tzong Chen, Tyng-Luh Liu |
| 2023 | Shapley Based Residual Decomposition for Instance Analysis. Tommy Liu, Amanda S. Barnard |
| 2023 | Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments. Runlong Zhou, Zihan Zhang, Simon Shaolei Du |
| 2023 | Sharper Bounds for ℓ David P. Woodruff, Taisuke Yasuda |
| 2023 | Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances. Ruben Ohana, Kimia Nadjahi, Alain Rakotomamonjy, Liva Ralaivola |
| 2023 | Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation. Matthew Raffel, Drew Penney, Lizhong Chen |
| 2023 | Short-lived High-volume Bandits. Su Jia, Nishant Oli, Ian Anderson, Paul Duff, Andrew A. Li, R. Ravi |
| 2023 | Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search. Xin Qiu, Risto Miikkulainen |
| 2023 | Simple Disentanglement of Style and Content in Visual Representations. Lilian Ngweta, Subha Maity, Alex Gittens, Yuekai Sun, Mikhail Yurochkin |
| 2023 | Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning. Evan Zheran Liu, Sahaana Suri, Tong Mu, Allan Zhou, Chelsea Finn |
| 2023 | Simple Hardware-Efficient Long Convolutions for Sequence Modeling. Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen, Armin W. Thomas, Michael Zhang, Tri Dao, Atri Rudra, Christopher Ré |
| 2023 | Simple and Fast Group Robustness by Automatic Feature Reweighting. Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson |
| 2023 | Simplex Random Features. Isaac Reid, Krzysztof Marcin Choromanski, Valerii Likhosherstov, Adrian Weller |
| 2023 | Simplified Temporal Consistency Reinforcement Learning. Yi Zhao, Wenshuai Zhao, Rinu Boney, Juho Kannala, Joni Pajarinen |
| 2023 | Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning. Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt |
| 2023 | SinDDM: A Single Image Denoising Diffusion Model. Vladimir Kulikov, Shahar Yadin, Matan Kleiner, Tomer Michaeli |
| 2023 | SinFusion: Training Diffusion Models on a Single Image or Video. Yaniv Nikankin, Niv Haim, Michal Irani |
| 2023 | Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function. Elissa Mhanna, Mohamad Assaad |
| 2023 | Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition. Dongqi Cai, Yangyuxuan Kang, Anbang Yao, Yurong Chen |
| 2023 | Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting. Jonathan Hehir, Daniel Ting, Graham Cormode |
| 2023 | Sketched Ridgeless Linear Regression: The Role of Downsampling. Xin Chen, Yicheng Zeng, Siyue Yang, Qiang Sun |
| 2023 | Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance. Zhao Song, Xin Yang, Yuanyuan Yang, Lichen Zhang |
| 2023 | Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. Zhao Song, Yitan Wang, Zheng Yu, Lichen Zhang |
| 2023 | Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty |
| 2023 | Slot-VAE: Object-Centric Scene Generation with Slot Attention. Yanbo Wang, Letao Liu, Justin Dauwels |
| 2023 | SlotGAT: Slot-based Message Passing for Heterogeneous Graphs. Ziang Zhou, Jieming Shi, Renchi Yang, Yuanhang Zou, Qing Li |
| 2023 | Smart Initial Basis Selection for Linear Programs. Zhenan Fan, Xinglu Wang, Oleksandr Yakovenko, Abdullah Ali Sivas, Owen Ren, Yong Zhang, Zirui Zhou |
| 2023 | Smooth Non-stationary Bandits. Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier |
| 2023 | SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. Guangxuan Xiao, Ji Lin, Mickaël Seznec, Hao Wu, Julien Demouth, Song Han |
| 2023 | Social learning spontaneously emerges by searching optimal heuristics with deep reinforcement learning. Seungwoong Ha, Hawoong Jeong |
| 2023 | Solving High-Dimensional PDEs with Latent Spectral Models. Haixu Wu, Tengge Hu, Huakun Luo, Jianmin Wang, Mingsheng Long |
| 2023 | Solving Linear Programs with Fast Online Learning Algorithms. Wenzhi Gao, Dongdong Ge, Chunlin Sun, Yinyu Ye |
| 2023 | SpENCNN: Orchestrating Encoding and Sparsity for Fast Homomorphically Encrypted Neural Network Inference. Ran Ran, Xinwei Luo, Wei Wang, Tao Liu, Gang Quan, Xiaolin Xu, Caiwen Ding, Wujie Wen |
| 2023 | Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach. Boya Hou, Sina Sanjari, Nathan Dahlin, Subhonmesh Bose, Umesh Vaidya |
| 2023 | SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot. Elias Frantar, Dan Alistarh |
| 2023 | SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge. Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh |
| 2023 | Spatial Implicit Neural Representations for Global-Scale Species Mapping. Elijah Cole, Grant Van Horn, Christian Lange, Alexander Shepard, Patrick Leary, Pietro Perona, Scott Loarie, Oisin Mac Aodha |
| 2023 | Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation. Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Siu Ming Yiu, Ruihua Han |
| 2023 | Special Properties of Gradient Descent with Large Learning Rates. Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich |
| 2023 | Specializing Smaller Language Models towards Multi-Step Reasoning. Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot |
| 2023 | Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary. Alexander Lindermayr, Nicole Megow, Martin Rapp |
| 2023 | SpeedDETR: Speed-aware Transformers for End-to-end Object Detection. Peiyan Dong, Zhenglun Kong, Xin Meng, Peng Zhang, Hao Tang, Yanzhi Wang, Chih-Hsien Chou |
| 2023 | Speeding Up Bellman Ford via Minimum Violation Permutations. Silvio Lattanzi, Ola Svensson, Sergei Vassilvitskii |
| 2023 | Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar |
| 2023 | Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. Louis C. Tiao, Vincent Dutordoir, Victor Picheny |
| 2023 | SpotEM: Efficient Video Search for Episodic Memory. Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman |
| 2023 | Spurious Valleys and Clustering Behavior of Neural Networks. Samuele Pollaci |
| 2023 | Stabilizing GANs' Training with Brownian Motion Controller. Tianjiao Luo, Ziyu Zhu, Jianfei Chen, Jun Zhu |
| 2023 | Stabilizing Transformer Training by Preventing Attention Entropy Collapse. Shuangfei Zhai, Tatiana Likhomanenko, Etai Littwin, Dan Busbridge, Jason Ramapuram, Yizhe Zhang, Jiatao Gu, Joshua M. Susskind |
| 2023 | Stable Estimation of Heterogeneous Treatment Effects. Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu |
| 2023 | Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning. Seungwook Kim, Chunghyun Park, Yoonwoo Jeong, Jaesik Park, Minsu Cho |
| 2023 | State and parameter learning with PARIS particle Gibbs. Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson |
| 2023 | Statistical Foundations of Prior-Data Fitted Networks. Thomas Nagler |
| 2023 | Statistical Indistinguishability of Learning Algorithms. Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas |
| 2023 | Statistical Inference and A/B Testing for First-Price Pacing Equilibria. Luofeng Liao, Christian Kroer |
| 2023 | Statistical Inference on Multi-armed Bandits with Delayed Feedback. Lei Shi, Jingshen Wang, Tianhao Wu |
| 2023 | Statistical Learning under Heterogenous Distribution Shift. Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy |
| 2023 | Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning. Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier |
| 2023 | Stochastic Gradient Descent under Markovian Sampling Schemes. Mathieu Even |
| 2023 | Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network. Farhad Pashakhanloo, Alexei A. Koulakov |
| 2023 | Stochastic Gradient Succeeds for Bandits. Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans |
| 2023 | Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels. Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf |
| 2023 | Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He |
| 2023 | Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola |
| 2023 | Strategic Classification with Unknown User Manipulations. Tosca Lechner, Ruth Urner, Shai Ben-David |
| 2023 | Stratified Adversarial Robustness with Rejection. Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, Yingyu Liang, Somesh Jha |
| 2023 | Streaming Active Learning with Deep Neural Networks. Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash |
| 2023 | Streaming Submodular Maximization with Differential Privacy. Anamay Chaturvedi, Huy L. Nguyen, Thy Dinh Nguyen |
| 2023 | StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes. Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan |
| 2023 | Structural Re-weighting Improves Graph Domain Adaptation. Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, Pan Li |
| 2023 | Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs. Dale Kim, Qing Zhou |
| 2023 | Structure-informed Language Models Are Protein Designers. Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu |
| 2023 | Structured Cooperative Learning with Graphical Model Priors. Shuangtong Li, Tianyi Zhou, Xinmei Tian, Dacheng Tao |
| 2023 | StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis. Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila |
| 2023 | Subequivariant Graph Reinforcement Learning in 3D Environments. Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang |
| 2023 | Submodular Order Functions and Assortment Optimization. Rajan Udwani |
| 2023 | Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation. Jin-Hong Du, Pratik Patil, Arun K. Kuchibhotla |
| 2023 | Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions. Niclas Boehmer, L. Elisa Celis, Lingxiao Huang, Anay Mehrotra, Nisheeth K. Vishnoi |
| 2023 | Subset-Based Instance Optimality in Private Estimation. Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh |
| 2023 | Superhuman Fairness. Omid Memarrast, Linh Vu, Brian D. Ziebart |
| 2023 | Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization. Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis |
| 2023 | Supported Trust Region Optimization for Offline Reinforcement Learning. Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji |
| 2023 | SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems. Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian |
| 2023 | SurProGenes: Survival Risk-Ordered Representation of Cancer Patients and Genes for the Identification of Prognostic Genes. Junetae Kim, Kyoungsuk Park, Hanseok Jeong, Youngwook Kim, Jeongseon Kim, Sun-Young Kim |
| 2023 | Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction. Yuan-Ting Hu, Alexander G. Schwing, Raymond A. Yeh |
| 2023 | Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. Junyi Zhu, Ruicong Yao, Matthew B. Blaschko |
| 2023 | Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks. Shikuang Deng, Hao Lin, Yuhang Li, Shi Gu |
| 2023 | Symmetry-Aware Robot Design with Structured Subgroups. Heng Dong, Junyu Zhang, Tonghan Wang, Chongjie Zhang |
| 2023 | Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand |
| 2023 | Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data. Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar |
| 2023 | Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models. Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen |
| 2023 | Synthetic data for model selection. Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, Matan Fintz, Gérard G. Medioni |
| 2023 | System Identification of Neural Systems: If We Got It Right, Would We Know? Yena Han, Tomaso A. Poggio, Brian Cheung |
| 2023 | TAN Without a Burn: Scaling Laws of DP-SGD. Tom Sander, Pierre Stock, Alexandre Sablayrolles |
| 2023 | TGRL: An Algorithm for Teacher Guided Reinforcement Learning. Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal |
| 2023 | TIDE: Time Derivative Diffusion for Deep Learning on Graphs. Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov |
| 2023 | TIPS: Topologically Important Path Sampling for Anytime Neural Networks. Guihong Li, Kartikeya Bhardwaj, Yuedong Yang, Radu Marculescu |
| 2023 | TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation. Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem |
| 2023 | TRAK: Attributing Model Behavior at Scale. Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, Aleksander Madry |
| 2023 | TabDDPM: Modelling Tabular Data with Diffusion Models. Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, Artem Babenko |
| 2023 | TabLeak: Tabular Data Leakage in Federated Learning. Mark Vero, Mislav Balunovic, Dimitar Iliev Dimitrov, Martin T. Vechev |
| 2023 | Taming graph kernels with random features. Krzysztof Marcin Choromanski |
| 2023 | Target-Aware Generative Augmentations for Single-Shot Adaptation. Kowshik Thopalli, Rakshith Subramanyam, Pavan K. Turaga, Jayaraman J. Thiagarajan |
| 2023 | Target-based Surrogates for Stochastic Optimization. Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux |
| 2023 | Task-Specific Skill Localization in Fine-tuned Language Models. Abhishek Panigrahi, Nikunj Saunshi, Haoyu Zhao, Sanjeev Arora |
| 2023 | Task-specific experimental design for treatment effect estimation. Bethany Connolly, Kim Moore, Tobias Schwedes, Alexander Adam, Gary Willis, Ilya Feige, Christopher Frye |
| 2023 | Taxonomy-Structured Domain Adaptation. Tianyi Liu, Zihao Xu, Hao He, Guang-Yuan Hao, Guang-He Lee, Hao Wang |
| 2023 | Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization. Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm |
| 2023 | Temporal Label Smoothing for Early Event Prediction. Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova |
| 2023 | Temporally Consistent Transformers for Video Generation. Wilson Yan, Danijar Hafner, Stephen James, Pieter Abbeel |
| 2023 | Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau |
| 2023 | Tensor Gaussian Process with Contraction for Multi-Channel Imaging Analysis. Hu Sun, Ward Manchester, Meng Jin, Yang Liu, Yang Chen |
| 2023 | Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization. Jungwuk Park, Dong-Jun Han, Soyeong Kim, Jaekyun Moon |
| 2023 | Test-time Adaptation with Slot-Centric Models. Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki |
| 2023 | Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise. Zhenghao Lin, Yeyun Gong, Yelong Shen, Tong Wu, Zhihao Fan, Chen Lin, Nan Duan, Weizhu Chen |
| 2023 | Text-To-4D Dynamic Scene Generation. Uriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual, Iurii Makarov, Filippos Kokkinos, Naman Goyal, Andrea Vedaldi, Devi Parikh, Justin Johnson, Yaniv Taigman |
| 2023 | Text-To-Concept (and Back) via Cross-Model Alignment. Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, Soheil Feizi |
| 2023 | The Acquisition of Physical Knowledge in Generative Neural Networks. Luca M. Schulze Buschoff, Eric Schulz, Marcel Binz |
| 2023 | The Benefits of Mixup for Feature Learning. Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu |
| 2023 | The Benefits of Model-Based Generalization in Reinforcement Learning. Kenny John Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber |
| 2023 | The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond. Jiin Woo, Gauri Joshi, Yuejie Chi |
| 2023 | The Catalog Problem: Clustering and Ordering Variable-Sized Sets. Mateusz Maria Jurewicz, Graham W. Taylor, Leon Derczynski |
| 2023 | The Computational Complexity of Concise Hypersphere Classification. Eduard Eiben, Robert Ganian, Iyad A. Kanj, Sebastian Ordyniak, Stefan Szeider |
| 2023 | The Dormant Neuron Phenomenon in Deep Reinforcement Learning. Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci |
| 2023 | The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. Pierre Harvey Richemond, Allison C. Tam, Yunhao Tang, Florian Strub, Bilal Piot, Felix Hill |
| 2023 | The Fast Johnson-Lindenstrauss Transform Is Even Faster. Ora Nova Fandina, Mikael Møller Høgsgaard, Kasper Green Larsen |
| 2023 | The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts |
| 2023 | The Hessian perspective into the Nature of Convolutional Neural Networks. Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf |
| 2023 | The Ideal Continual Learner: An Agent That Never Forgets. Liangzu Peng, Paris Giampouras, René Vidal |
| 2023 | The Impact of Exploration on Convergence and Performance of Multi-Agent Q-Learning Dynamics. Aamal Abbas Hussain, Francesco Belardinelli, Dario Paccagnan |
| 2023 | The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent. Lei Wu, Weijie J. Su |
| 2023 | The Monge Gap: A Regularizer to Learn All Transport Maps. Théo Uscidda, Marco Cuturi |
| 2023 | The Numerical Stability of Hyperbolic Representation Learning. Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang |
| 2023 | The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. Philip Amortila, Nan Jiang, Csaba Szepesvári |
| 2023 | The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data. Thomas Davies, Zhengchao Wan, Rubén J. Sánchez-García |
| 2023 | The Power of Learned Locally Linear Models for Nonlinear Policy Optimization. Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu |
| 2023 | The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing. Xingyu Xu, Yandi Shen, Yuejie Chi, Cong Ma |
| 2023 | The Power of Uniform Sampling for k-Median. Lingxiao Huang, Shaofeng H.-C. Jiang, Jianing Lou |
| 2023 | The Price of Differential Privacy under Continual Observation. Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. Smith |
| 2023 | The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning. Victor Boone, Bruno Gaujal |
| 2023 | The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. Borja Rodríguez Gálvez, Arno Blaas, Pau Rodríguez, Adam Golinski, Xavier Suau, Jason Ramapuram, Dan Busbridge, Luca Zappella |
| 2023 | The SSL Interplay: Augmentations, Inductive Bias, and Generalization. Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti |
| 2023 | The Saddle-Point Method in Differential Privacy. Wael Alghamdi, Juan Felipe Gómez, Shahab Asoodeh, Flávio P. Calmon, Oliver Kosut, Lalitha Sankar |
| 2023 | The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney |
| 2023 | The Statistical Scope of Multicalibration. Georgy Noarov, Aaron Roth |
| 2023 | The Test of Tests: A Framework for Differentially Private Hypothesis Testing. Zeki Kazan, Kaiyan Shi, Adam Groce, Andrew P. Bray |
| 2023 | The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning. Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan A. Murphy, Finale Doshi-Velez |
| 2023 | The Unreasonable Effectiveness of Few-shot Learning for Machine Translation. Xavier Garcia, Yamini Bansal, Colin Cherry, George F. Foster, Maxim Krikun, Melvin Johnson, Orhan Firat |
| 2023 | The Value of Out-of-Distribution Data. Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein |
| 2023 | The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms. Anirudh Vemula, Yuda Song, Aarti Singh, Drew Bagnell, Sanjiban Choudhury |
| 2023 | The Wisdom of Hindsight Makes Language Models Better Instruction Followers. Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez |
| 2023 | The case for 4-bit precision: k-bit Inference Scaling Laws. Tim Dettmers, Luke Zettlemoyer |
| 2023 | Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables. Rick Wilming, Leo Kieslich, Benedict Clark, Stefan Haufe |
| 2023 | Theoretical Bounds on the Network Community Profile from Low-rank Semi-definite Programming. Yufan Huang, C. Seshadhri, David F. Gleich |
| 2023 | Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park |
| 2023 | Theory on Forgetting and Generalization of Continual Learning. Sen Lin, Peizhong Ju, Yingbin Liang, Ness B. Shroff |
| 2023 | Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits. Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari |
| 2023 | Thompson Sampling with Diffusion Generative Prior. Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton, Patrick Blöbaum |
| 2023 | Thompson Sampling with Less Exploration is Fast and Optimal. Tianyuan Jin, Xianglin Yang, Xiaokui Xiao, Pan Xu |
| 2023 | Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk |
| 2023 | Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations. Hong-Ming Chiu, Richard Y. Zhang |
| 2023 | Tight Data Access Bounds for Private Top-k Selection. Hao Wu, Olga Ohrimenko, Anthony Wirth |
| 2023 | Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits. Chen Wang |
| 2023 | Tight and fast generalization error bound of graph embedding in metric space. Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi |
| 2023 | Tighter Analysis for ProxSkip. Zhengmian Hu, Heng Huang |
| 2023 | Tighter Bounds on the Expressivity of Transformer Encoders. David Chiang, Peter Cholak, Anand Pillay |
| 2023 | Tighter Information-Theoretic Generalization Bounds from Supersamples. Ziqiao Wang, Yongyi Mao |
| 2023 | Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond. Jaeyoung Cha, Jaewook Lee, Chulhee Yun |
| 2023 | Tilted Sparse Additive Models. Yingjie Wang, Hong Chen, Weifeng Liu, Fengxiang He, Tieliang Gong, Youcheng Fu, Dacheng Tao |
| 2023 | Topological Point Cloud Clustering. Vincent Peter Grande, Michael T. Schaub |
| 2023 | Topological Singularity Detection at Multiple Scales. Julius von Rohrscheidt, Bastian Rieck |
| 2023 | Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes. Nico Stucki, Johannes C. Paetzold, Suprosanna Shit, Bjoern H. Menze, Ulrich Bauer |
| 2023 | Total Variation Graph Neural Networks. Jonas Berg Hansen, Filippo Maria Bianchi |
| 2023 | Toward Efficient Gradient-Based Value Estimation. Arsalan Sharifnassab, Richard S. Sutton |
| 2023 | Toward Large Kernel Models. Amirhesam Abedsoltan, Mikhail Belkin, Parthe Pandit |
| 2023 | Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering. Mingqi Yang, Wenjie Feng, Yanming Shen, Bryan Hooi |
| 2023 | Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten. Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju |
| 2023 | Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models. Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi S. Jaakkola, Shiyu Chang |
| 2023 | Towards Constituting Mathematical Structures for Learning to Optimize. Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin, HanQin Cai |
| 2023 | Towards Controlled Data Augmentations for Active Learning. Jianan Yang, Haobo Wang, Sai Wu, Gang Chen, Junbo Zhao |
| 2023 | Towards Deep Attention in Graph Neural Networks: Problems and Remedies. Soo Yong Lee, Fanchen Bu, Jaemin Yoo, Kijung Shin |
| 2023 | Towards Explaining Distribution Shifts. Sean Kulinski, David I. Inouye |
| 2023 | Towards Learning Geometric Eigen-Lengths Crucial for Fitting Tasks. Yijia Weng, Kaichun Mo, Ruoxi Shi, Yanchao Yang, Leonidas J. Guibas |
| 2023 | Towards Omni-generalizable Neural Methods for Vehicle Routing Problems. Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang |
| 2023 | Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes. Shion Takeno, Masahiro Nomura, Masayuki Karasuyama |
| 2023 | Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver. Xinyu Ye, Ge Yan, Junchi Yan |
| 2023 | Towards Reliable Neural Specifications. Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si |
| 2023 | Towards Robust Graph Incremental Learning on Evolving Graphs. Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu |
| 2023 | Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data. Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Yihang Yao, Hanjiang Hu, Ding Zhao |
| 2023 | Towards Stable and Efficient Adversarial Training against l Yulun Jiang, Chen Liu, Zhichao Huang, Mathieu Salzmann, Sabine Süsstrunk |
| 2023 | Towards Sustainable Learning: Coresets for Data-efficient Deep Learning. Yu Yang, Hao Kang, Baharan Mirzasoleiman |
| 2023 | Towards Theoretical Understanding of Inverse Reinforcement Learning. Alberto Maria Metelli, Filippo Lazzati, Marcello Restelli |
| 2023 | Towards Trustworthy Explanation: On Causal Rationalization. Wenbo Zhang, Tong Wu, Yunlong Wang, Yong Cai, Hengrui Cai |
| 2023 | Towards Unbiased Training in Federated Open-world Semi-supervised Learning. Jie Zhang, Xiaosong Ma, Song Guo, Wenchao Xu |
| 2023 | Towards Understanding Ensemble Distillation in Federated Learning. Sejun Park, Kihun Hong, Ganguk Hwang |
| 2023 | Towards Understanding Generalization of Graph Neural Networks. Huayi Tang, Yong Liu |
| 2023 | Towards Understanding Generalization of Macro-AUC in Multi-label Learning. Guoqiang Wu, Chongxuan Li, Yilong Yin |
| 2023 | Towards Understanding and Improving GFlowNet Training. Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali, Andreas Loukas, Kyunghyun Cho, Tommaso Biancalani |
| 2023 | Towards Understanding and Reducing Graph Structural Noise for GNNs. Mingze Dong, Yuval Kluger |
| 2023 | Towards a Persistence Diagram that is Robust to Noise and Varied Densities. Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu |
| 2023 | Towards a better understanding of representation dynamics under TD-learning. Yunhao Tang, Rémi Munos |
| 2023 | Towards credible visual model interpretation with path attribution. Naveed Akhtar, Mohammad A. A. K. Jalwana |
| 2023 | Tractable Control for Autoregressive Language Generation. Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van den Broeck |
| 2023 | Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts. Dirk van der Hoeven, Ciara Pike-Burke, Hao Qiu, Nicolò Cesa-Bianchi |
| 2023 | Trainability, Expressivity and Interpretability in Gated Neural ODEs. Timothy Doyeon Kim, Tankut Can, Kamesh Krishnamurthy |
| 2023 | Training Deep Surrogate Models with Large Scale Online Learning. Lucas Thibaut Meyer, Marc Schouler, Robert Alexander Caulk, Alejandro Ribés, Bruno Raffin |
| 2023 | Training Normalizing Flows from Dependent Data. Matthias Kirchler, Christoph Lippert, Marius Kloft |
| 2023 | Training-Free Neural Active Learning with Initialization-Robustness Guarantees. Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh, See-Kiong Ng, Bryan Kian Hsiang Low |
| 2023 | Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning. Brett Daley, Martha White, Christopher Amato, Marlos C. Machado |
| 2023 | Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving. Weixin Li, Xiaodong Yang |
| 2023 | Transformed Distribution Matching for Missing Value Imputation. He Zhao, Ke Sun, Amir Dezfouli, Edwin V. Bonilla |
| 2023 | Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization. Chanyeong Kim, Jongwoong Park, Hyunglip Bae, Woo Chang Kim |
| 2023 | Transformers Learn In-Context by Gradient Descent. Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov |
| 2023 | Transformers Meet Directed Graphs. Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru |
| 2023 | Transformers as Algorithms: Generalization and Stability in In-context Learning. Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, Samet Oymak |
| 2023 | Trapdoor Normalization with Irreversible Ownership Verification. Hanwen Liu, Zhenyu Weng, Yuesheng Zhu, Yadong Mu |
| 2023 | Traversing Between Modes in Function Space for Fast Ensembling. EungGu Yun, Hyungi Lee, Giung Nam, Juho Lee |
| 2023 | Trompt: Towards a Better Deep Neural Network for Tabular Data. Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Ting-Wei Chen, Tien-Hao Chang |
| 2023 | Truncating Trajectories in Monte Carlo Reinforcement Learning. Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli |
| 2023 | Trustworthy Policy Learning under the Counterfactual No-Harm Criterion. Haoxuan Li, Chunyuan Zheng, Yixiao Cao, Zhi Geng, Yue Liu, Peng Wu |
| 2023 | Tuning Computer Vision Models With Task Rewards. André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai |
| 2023 | Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning. Yu Meng, Martin Michalski, Jiaxin Huang, Yu Zhang, Tarek F. Abdelzaher, Jiawei Han |
| 2023 | Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach |
| 2023 | Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective. Yulong Lu |
| 2023 | UMD: Unsupervised Model Detection for X2X Backdoor Attacks. Zhen Xiang, Zidi Xiong, Bo Li |
| 2023 | UPSCALE: Unconstrained Channel Pruning. Alvin Wan, Hanxiang Hao, Kaushik Patnaik, Yueyang Xu, Omer Hadad, David Güera, Zhile Ren, Qi Shan |
| 2023 | UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers. Dachuan Shi, Chaofan Tao, Ying Jin, Zhendong Yang, Chun Yuan, Jiaqi Wang |
| 2023 | Uncertain Evidence in Probabilistic Models and Stochastic Simulators. Andreas Munk, Alexander Mead, Frank Wood |
| 2023 | Uncertainty Estimation by Fisher Information-based Evidential Deep Learning. Danruo Deng, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng |
| 2023 | Uncertainty Estimation for Molecules: Desiderata and Methods. Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann |
| 2023 | Unconstrained Online Learning with Unbounded Losses. Andrew Jacobsen, Ashok Cutkosky |
| 2023 | Uncovering Adversarial Risks of Test-Time Adaptation. Tong Wu, Feiran Jia, Xiangyu Qi, Jiachen T. Wang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal |
| 2023 | Under-Counted Tensor Completion with Neural Incorporation of Attributes. Shahana Ibrahim, Xiao Fu, Rebecca A. Hutchinson, Eugene Seo |
| 2023 | Understand and Modularize Generator Optimization in ELECTRA-style Pretraining. Chengyu Dong, Liyuan Liu, Hao Cheng, Jingbo Shang, Jianfeng Gao, Xiaodong Liu |
| 2023 | Understanding Backdoor Attacks through the Adaptability Hypothesis. Xun Xian, Ganghua Wang, Jayanth Srinivasa, Ashish Kundu, Xuan Bi, Mingyi Hong, Jie Ding |
| 2023 | Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias. Ryo Karakida, Tomoumi Takase, Tomohiro Hayase, Kazuki Osawa |
| 2023 | Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee |
| 2023 | Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases. Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, Yuxiong He |
| 2023 | Understanding Oversquashing in GNNs through the Lens of Effective Resistance. Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang |
| 2023 | Understanding Plasticity in Neural Networks. Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Ávila Pires, Razvan Pascanu, Will Dabney |
| 2023 | Understanding Self-Distillation in the Presence of Label Noise. Rudrajit Das, Sujay Sanghavi |
| 2023 | Understanding Self-Predictive Learning for Reinforcement Learning. Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko |
| 2023 | Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer. Liang Liu, Yanan Guo, Youtao Zhang, Jun Yang |
| 2023 | Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective. Liangliang Shi, Gu Zhang, Haoyu Zhen, Jintao Fan, Junchi Yan |
| 2023 | Understanding the Complexity Gains of Single-Task RL with a Curriculum. Qiyang Li, Yuexiang Zhai, Yi Ma, Sergey Levine |
| 2023 | Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits. Xuejie Liu, Anji Liu, Guy Van den Broeck, Yitao Liang |
| 2023 | Understanding the Impact of Adversarial Robustness on Accuracy Disparity. Yuzheng Hu, Fan Wu, Hongyang Zhang, Han Zhao |
| 2023 | Understanding the Role of Feedback in Online Learning with Switching Costs. Duo Cheng, Xingyu Zhou, Bo Ji |
| 2023 | Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data. Ali Siahkoohi, Rudy Morel, Maarten V. de Hoop, Erwan Allys, Grégory Sainton, Taichi Kawamura |
| 2023 | Unifying Molecular and Textual Representations via Multi-task Language Modelling. Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, Matteo Manica |
| 2023 | Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions. Jungbin Kim, Insoon Yang |
| 2023 | Unit Scaling: Out-of-the-Box Low-Precision Training. Charlie Blake, Douglas Orr, Carlo Luschi |
| 2023 | Universal Morphology Control via Contextual Modulation. Zheng Xiong, Jacob Beck, Shimon Whiteson |
| 2023 | Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data. Lena Podina, Brydon Eastman, Mohammad Kohandel |
| 2023 | Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability. Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, Bo Han |
| 2023 | Unlocking Slot Attention by Changing Optimal Transport Costs. Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek |
| 2023 | Unscented Autoencoder. Faris Janjos, Lars Rosenbaum, Maxim Dolgov, J. Marius Zoellner |
| 2023 | Unsupervised Out-of-Distribution Detection with Diffusion Inpainting. Zhenzhen Liu, Jin Peng Zhou, Yufan Wang, Kilian Q. Weinberger |
| 2023 | Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments. Sang-Hyun Lee, Seung-Woo Seo |
| 2023 | Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features. Chieh Hubert Lin, Hung-Yu Tseng, Hsin-Ying Lee, Maneesh Kumar Singh, Ming-Hsuan Yang |
| 2023 | Unveiling the Latent Space Geometry of Push-Forward Generative Models. Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary, David Picard |
| 2023 | User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez |
| 2023 | User-level Private Stochastic Convex Optimization with Optimal Rates. Raef Bassily, Ziteng Sun |
| 2023 | Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies. Gati V. Aher, Rosa I. Arriaga, Adam Tauman Kalai |
| 2023 | Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy. Xing Liu, Andrew B. Duncan, Axel Gandy |
| 2023 | VA-learning as a more efficient alternative to Q-learning. Yunhao Tang, Rémi Munos, Mark Rowland, Michal Valko |
| 2023 | VIMA: Robot Manipulation with Multimodal Prompts. Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, Linxi Fan |
| 2023 | Variance Control for Distributional Reinforcement Learning. Qi Kuang, Zhoufan Zhu, Liwen Zhang, Fan Zhou |
| 2023 | Variational Autoencoding Neural Operators. Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris |
| 2023 | Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills. Seongun Kim, Kyowoon Lee, Jaesik Choi |
| 2023 | Variational Mixture of HyperGenerators for Learning Distributions over Functions. Batuhan Koyuncu, Pablo Sánchez-Martín, Ignacio Peis, Pablo M. Olmos, Isabel Valera |
| 2023 | Variational Open-Domain Question Answering. Valentin Liévin, Andreas Geert Motzfeldt, Ida Riis Jensen, Ole Winther |
| 2023 | Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization. Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Tobias Schäfer, Matthias Katzfuss |
| 2023 | Vector Quantized Wasserstein Auto-Encoder. Long Tung Vuong, Trung Le, He Zhao, Chuanxia Zheng, Mehrtash Harandi, Jianfei Cai, Dinh Q. Phung |
| 2023 | Vector-Valued Control Variates. Zhuo Sun, Alessandro Barp, François-Xavier Briol |
| 2023 | VectorMapNet: End-to-end Vectorized HD Map Learning. Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao |
| 2023 | Vertical Federated Graph Neural Network for Recommender System. Peihua Mai, Yan Pang |
| 2023 | Von Mises Mixture Distributions for Molecular Conformation Generation. Kirk Swanson, Jake Lawrence Williams, Eric M. Jonas |
| 2023 | WL meet VC. Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe |
| 2023 | Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap. Hang Wang, Sen Lin, Junshan Zhang |
| 2023 | Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks. Xu Chu, Yujie Jin, Xin Wang, Shanghang Zhang, Yasha Wang, Wenwu Zhu, Hong Mei |
| 2023 | Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes. Zhaowei Zhu, Yuanshun Yao, Jiankai Sun, Hang Li, Yang Liu |
| 2023 | Weakly Supervised Regression with Interval Targets. Xin Cheng, Yuzhou Cao, Ximing Li, Bo An, Lei Feng |
| 2023 | Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees. Shenghao Yang, Kimon Fountoulakis |
| 2023 | Weighted Sampling without Replacement for Deep Top-k Classification. Dieqiao Feng, Yuanqi Du, Carla P. Gomes, Bart Selman |
| 2023 | Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality. Dhruv Malik, Conor Igoe, Yuanzhi Li, Aarti Singh |
| 2023 | What Can Be Learnt With Wide Convolutional Neural Networks? Francesco Cagnetta, Alessandro Favero, Matthieu Wyart |
| 2023 | What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings. Zequn Sun, Jiacheng Huang, Xiaozhou Xu, Qijin Chen, Weijun Ren, Wei Hu |
| 2023 | What can online reinforcement learning with function approximation benefit from general coverage conditions? Fanghui Liu, Luca Viano, Volkan Cevher |
| 2023 | What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective. Rhea Chowers, Yair Weiss |
| 2023 | What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? Rui Yang, Lin Yong, Xiaoteng Ma, Hao Hu, Chongjie Zhang, Tong Zhang |
| 2023 | When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction. Vinith Menon Suriyakumar, Marzyeh Ghassemi, Berk Ustun |
| 2023 | When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations. Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, Chuxu Zhang |
| 2023 | When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis. Yiyou Sun, Zhenmei Shi, Yingyu Liang, Yixuan Li |
| 2023 | When do Minimax-fair Learning and Empirical Risk Minimization Coincide? Harvineet Singh, Matthäus Kleindessner, Volkan Cevher, Rumi Chunara, Chris Russell |
| 2023 | When does Privileged information Explain Away Label Noise? Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander Nicholas D'Amour, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou |
| 2023 | When is Realizability Sufficient for Off-Policy Reinforcement Learning? Andrea Zanette |
| 2023 | Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression. Yihao Xue, Siddharth Joshi, Eric Gan, Pin-Yu Chen, Baharan Mirzasoleiman |
| 2023 | Which Invariance Should We Transfer? A Causal Minimax Learning Approach. Mingzhou Liu, Xiangyu Zheng, Xinwei Sun, Fang Fang, Yizhou Wang |
| 2023 | Which Tricks are Important for Learning to Rank? Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin, Liudmila Prokhorenkova |
| 2023 | Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu |
| 2023 | Who Needs to Know? Minimal Knowledge for Optimal Coordination. Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael D. Dennis, Stuart Russell |
| 2023 | Whose Opinions Do Language Models Reflect? Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, Tatsunori Hashimoto |
| 2023 | Why Is Public Pretraining Necessary for Private Model Training? Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang |
| 2023 | Why Random Pruning Is All We Need to Start Sparse. Advait Harshal Gadhikar, Sohom Mukherjee, Rebekka Burkholz |
| 2023 | Why Target Networks Stabilise Temporal Difference Methods. Mattie Fellows, Matthew J. A. Smith, Shimon Whiteson |
| 2023 | Why do Nearest Neighbor Language Models Work? Frank F. Xu, Uri Alon, Graham Neubig |
| 2023 | Why does Throwing Away Data Improve Worst-Group Error? Kamalika Chaudhuri, Kartik Ahuja, Martín Arjovsky, David Lopez-Paz |
| 2023 | Width and Depth Limits Commute in Residual Networks. Soufiane Hayou, Greg Yang |
| 2023 | Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition. Boran Han |
| 2023 | X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion. Hanqing Zhao, Dianmo Sheng, Jianmin Bao, Dongdong Chen, Dong Chen, Fang Wen, Lu Yuan, Ce Liu, Wenbo Zhou, Qi Chu, Weiming Zhang, Nenghai Yu |
| 2023 | XTab: Cross-table Pretraining for Tabular Transformers. Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran |
| 2023 | dugMatting: Decomposed-Uncertainty-Guided Matting. Jiawei Wu, Changqing Zhang, Zuoyong Li, Huazhu Fu, Xi Peng, Joey Tianyi Zhou |
| 2023 | mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video. Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi, Qi Qian, Wei Wang, Guohai Xu, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou |
| 2023 | simple diffusion: End-to-end diffusion for high resolution images. Emiel Hoogeboom, Jonathan Heek, Tim Salimans |
| 2023 | spred: Solving L1 Penalty with SGD. Liu Ziyin, Zihao Wang |
| 2023 | π-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation. Chengyue Wu, Teng Wang, Yixiao Ge, Zeyu Lu, Ruisong Zhou, Ying Shan, Ping Luo |