Du, Simon

30 publications

COLT 2025 Anytime Acceleration of Gradient Descent Zihan Zhang, Jason Lee, Simon Du, Yuxin Chen

AISTATS 2023 Blessing of Class Diversity in Pre-Training Yulai Zhao, Jianshu Chen, Simon Du

COLT 2023 Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation Qiwen Cui, Kaiqing Zhang, Simon Du

NeurIPSW 2023 JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention Yuandong Tian, Yiping Wang, Zhenyu Zhang, Beidi Chen, Simon Du

COLT 2023 Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron Weihang Xu, Simon Du

AISTATS 2022 Gap-Dependent Bounds for Two-Player Markov Games Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon Du

AISTATS 2022 Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games Yulai Zhao, Yuandong Tian, Jason Lee, Simon Du

ICML 2022 Active Multi-Task Representation Learning Yifang Chen, Kevin Jamieson, Simon Du

ICML 2022 Denoised MDPs: Learning World Models Better than the World Itself Tongzhou Wang, Simon Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian

ICML 2022 First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach Andrew J Wagenmaker, Yifang Chen, Max Simchowitz, Simon Du, Kevin Jamieson

COLT 2022 Horizon-Free Reinforcement Learning in Polynomial Time: The Power of Stationary Policies Zihan Zhang, Xiangyang Ji, Simon Du

ICML 2022 Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path Haoyuan Cai, Tengyu Ma, Simon Du

ICML 2022 Nearly Optimal Policy Optimization with Stable at Any Time Guarantee Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon Du, Jiantao Jiao

ICML 2022 Reward-Free RL Is No Harder than Reward-Aware RL in Linear Markov Decision Processes Andrew J Wagenmaker, Yifang Chen, Max Simchowitz, Simon Du, Kevin Jamieson

AISTATS 2021 Q-Learning with Logarithmic Regret Kunhe Yang, Lin Yang, Simon Du

ICML 2021 Bilinear Classes: A Structural Framework for Provable Generalization in RL Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang

COLT 2021 Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap Haike Xu, Tengyu Ma, Simon Du

ICML 2021 Improved Corruption Robust Algorithms for Episodic Reinforcement Learning Yifang Chen, Simon Du, Kevin Jamieson

COLT 2021 Is Reinforcement Learning More Difficult than Bandits? a Near-Optimal Algorithm Escaping the Curse of Horizon Zihan Zhang, Xiangyang Ji, Simon Du

ICML 2021 Near Optimal Reward-Free Reinforcement Learning Zihan Zhang, Simon Du, Xiangyang Ji

ICML 2021 On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP Tianhao Wu, Yunchang Yang, Simon Du, Liwei Wang

ICML 2020 Provable Representation Learning for Imitation Learning via Bi-Level Optimization Sanjeev Arora, Simon Du, Sham Kakade, Yuping Luo, Nikunj Saunshi

ICML 2019 Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, Ruosong Wang

ICML 2019 Gradient Descent Finds Global Minima of Deep Neural Networks Simon Du, Jason Lee, Haochuan Li, Liwei Wang, Xiyu Zhai

ICML 2019 Provably Efficient RL with Rich Observations via Latent State Decoding Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, John Langford

ICML 2019 Width Provably Matters in Optimization for Deep Linear Neural Networks Simon Du, Wei Hu

ICML 2018 Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell

ICML 2018 Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow Xiao Zhang, Simon Du, Quanquan Gu

ICML 2018 Gradient Descent Learns One-Hidden-Layer CNN: Don’t Be Afraid of Spurious Local Minima Simon Du, Jason Lee, Yuandong Tian, Aarti Singh, Barnabas Poczos

ICML 2018 On the Power of Over-Parametrization in Neural Networks with Quadratic Activation Simon Du, Jason Lee