ML Anthology

Du, Simon S.

53 publications

NeurIPS 2024 Decoding-Time Language Model Alignment with Multiple Objectives Ruizhe Shi, Yifang Chen, Yushi Hu, Alisa Liu, Hannaneh Hajishirzi, Noah A. Smith, Simon S. Du
NeurIPS 2024 Learning Optimal Tax Design in Nonatomic Congestion Games Qiwen Cui, Maryam Fazel, Simon S. Du
NeurIPS 2024 Learning to Cooperate with Humans Using Generative Agents Yancheng Liang, Daphne Chen, Abhishek Gupta, Simon S. Du, Natasha Jaques
COLT 2024 Optimal Multi-Distribution Learning Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S Du, Jason D Lee
COLT 2024 Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract) Yan Dai, Qiwen Cui, Simon S. Du
COLT 2024 Settling the Sample Complexity of Online Reinforcement Learning Zihan Zhang, Yuxin Chen, Jason D Lee, Simon S Du
NeurIPS 2024 Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models Weihang Xu, Maryam Fazel, Simon S. Du
NeurIPS 2024 Understanding the Gains from Repeated Self-Distillation Divyansh Pareek, Simon S. Du, Sewoong Oh
NeurIPS 2023 A Reduction-Based Framework for Sequential Decision Making with Delayed Feedback Yunchang Yang, Han Zhong, Tianhao Wu, Bin Liu, Liwei Wang, Simon S Du
NeurIPS 2023 Active Representation Learning for General Task Space with Applications in Robotics Yifang Chen, Yingbing Huang, Simon S Du, Kevin G. Jamieson, Guanya Shi
NeurIPS 2023 Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S Du
NeurIPS 2023 Scan and Snap: Understanding Training Dynamics and Token Composition in 1-Layer Transformer Yuandong Tian, Yiping Wang, Beidi Chen, Simon S Du
NeurIPS 2022 Learning in Congestion Games with Bandit Feedback Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S Du
NeurIPS 2022 Near-Optimal Randomized Exploration for Tabular Markov Decision Processes Zhihan Xiong, Ruoqi Shen, Qiwen Cui, Maryam Fazel, Simon S Du
NeurIPS 2022 On Gap-Dependent Bounds for Offline Reinforcement Learning Xinqi Wang, Qiwen Cui, Simon S Du
NeurIPS 2022 Provable General Function Class Representation Learning in Multitask Bandits and MDP Rui Lu, Andrew Zhao, Simon S Du, Gao Huang
NeurIPS 2022 Provably Efficient Offline Multi-Agent Reinforcement Learning via Strategy-Wise Bonus Qiwen Cui, Simon S Du
NeurIPS 2022 When Are Offline Two-Player Zero-Sum Markov Games Solvable? Qiwen Cui, Simon S Du
NeurIPS 2021 Corruption Robust Active Learning Yifang Chen, Simon S Du, Kevin G. Jamieson
NeurIPS 2021 Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization Tian Ye, Simon S Du
NeurIPS 2021 Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S Du
NeurIPS 2021 Nearly Horizon-Free Offline Reinforcement Learning Tongzheng Ren, Jialian Li, Bo Dai, Simon S Du, Sujay Sanghavi
NeurIPS 2021 Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret Jean Tarbouriech, Runlong Zhou, Simon S Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
UAI 2021 When Is Particle Filtering Efficient for Planning in Partially Observed Linear Dynamical Systems? Simon S. Du, Wei Hu, Zhiyuan Li, Ruoqi Shen, Zhao Song, Jiajun Wu
NeurIPS 2020 Agnostic $q$-Learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity Simon S Du, Jason Lee, Gaurav Mahajan, Ruosong Wang
IJCAI 2020 DualSMC: Tunneling Differentiable Filtering and Planning Under Continuous POMDPs Yunbo Wang, Bo Liu, Jiajun Wu, Yuke Zhu, Simon S. Du, Li Fei-Fei, Joshua B. Tenenbaum
ICLR 2020 Harnessing the Power of Infinitely Wide Deep Nets on Small-Data Tasks Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
NeurIPS 2020 Is Long Horizon RL More Difficult than Short Horizon RL? Ruosong Wang, Simon S Du, Lin Yang, Sham Kakade
ICLR 2020 Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang
NeurIPS 2020 On Reward-Free Reinforcement Learning with Linear Function Approximation Ruosong Wang, Simon S Du, Lin Yang, Ruslan Salakhutdinov
JMLR 2020 On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics Xi Chen, Simon S. Du, Xin T. Tong
NeurIPS 2020 Over-Parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality Yi Zhang, Orestis Plevrakis, Simon S Du, Xingguo Li, Zhao Song, Sanjeev Arora
NeurIPS 2020 Planning with General Objective Functions: Going Beyond Total Rewards Ruosong Wang, Peilin Zhong, Simon S Du, Ruslan Salakhutdinov, Lin Yang
NeurIPS 2020 Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning Fei Feng, Ruosong Wang, Wotao Yin, Simon S Du, Lin Yang
ICLR 2020 What Can Neural Networks Reason About? Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka
NeurIPS 2019 Acceleration via Symplectic Discretization of High-Resolution Differential Equations Bin Shi, Simon S Du, Weijie Su, Michael I Jordan
ICLR 2019 Gradient Descent Provably Optimizes Over-Parameterized Neural Networks Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh
NeurIPS 2019 Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels Simon S Du, Kangcheng Hou, Ruslan Salakhutdinov, Barnabas Poczos, Ruosong Wang, Keyulu Xu
AISTATS 2019 Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems Without Strong Convexity Simon S. Du, Wei Hu
NeurIPS 2019 On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
NeurIPS 2019 Provably Efficient Q-Learning with Function Approximation via Distribution Shift Error Checking Oracle Simon S Du, Yuping Luo, Ruosong Wang, Hanrui Zhang
NeurIPS 2019 Towards Understanding the Importance of Shortcut Connections in Residual Networks Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S Du, Enlu Zhou, Tuo Zhao
NeurIPS 2018 Algorithmic Regularization in Learning Deep Homogeneous Models: Layers Are Automatically Balanced Simon S Du, Wei Hu, Jason Lee
NeurIPS 2018 How Many Samples Are Needed to Estimate a Convolutional Neural Network? Simon S Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov, Aarti Singh
AISTATS 2018 Stochastic Zeroth-Order Optimization in High Dimensions Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
ICLR 2018 When Is a Convolutional Filter Easy to Learn? Simon S. Du, Jason D. Lee, Yuandong Tian
COLT 2017 Computationally Efficient Robust Sparse Estimation in High Dimensions Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh
NeurIPS 2017 Gradient Descent Can Take Exponential Time to Escape Saddle Points Simon S Du, Chi Jin, Jason Lee, Michael I Jordan, Aarti Singh, Barnabas Poczos
NeurIPS 2017 Hypothesis Transfer Learning via Transformation Functions Simon S Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos
NeurIPS 2017 On the Power of Truncated SVD for General High-Rank Matrix Estimation Problems Simon S Du, Yining Wang, Aarti Singh
ICML 2017 Stochastic Variance Reduction Methods for Policy Evaluation Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou
NeurIPS 2016 Efficient Nonparametric Smoothness Estimation Shashank Singh, Simon S Du, Barnabas Poczos
AISTATS 2015 Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method David G. Anderson, Simon S. Du, Michael W. Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu