| 2025 | CogSci | Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination. | Kunal Jha, Wilka Carvalho, Yancheng Liang, Simon S. Du, Natasha Jaques, Max Kleiman-Weiner |
| 2025 | COLT | Anytime Acceleration of Gradient Descent. | Zihan Zhang, Jason D. Lee, Simon S. Du, Yuxin Chen |
| 2024 | ACL | An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. | Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak |
| 2024 | ACL | Reflect-RL: Two-Player Online RL Fine-Tuning for LMs. | Runlong Zhou, Simon S. Du, Beibin Li |
| 2024 | COLT | Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract). | Yan Dai, Qiwen Cui, Simon S. Du |
| 2024 | COLT | Settling the sample complexity of online reinforcement learning. | Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du |
| 2024 | COLT | Optimal Multi-Distribution Learning. | Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee |
| 2023 | AISTATS | Blessing of Class Diversity in Pre-training. | Yulai Zhao, Jianshu Chen, Simon S. Du |
| 2023 | COLT | Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation. | Qiwen Cui, Kaiqing Zhang, Simon S. Du |
| 2023 | COLT | Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron. | Weihang Xu, Simon S. Du |
| 2022 | AISTATS | Gap-Dependent Bounds for Two-Player Markov Games. | Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon S. Du |
| 2022 | AISTATS | Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. | Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du |
| 2022 | COLT | Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies. | Zihan Zhang, Xiangyang Ji, Simon S. Du |
| 2022 | ICML | Denoised MDPs: Learning World Models Better Than the World Itself. | Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian |
| 2022 | ICML | Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path. | Haoyuan Cai, Tengyu Ma, Simon S. Du |
| 2022 | ICML | Active Multi-Task Representation Learning. | Yifang Chen, Kevin Jamieson, Simon S. Du |
| 2022 | ICML | First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. | Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson |
| 2022 | ICML | Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. | Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson |
| 2022 | ICML | Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. | Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao |
| 2021 | AISTATS | Q-learning with Logarithmic Regret. | Kunhe Yang, Lin F. Yang, Simon S. Du |
| 2021 | COLT | Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap. | Haike Xu, Tengyu Ma, Simon S. Du |
| 2021 | COLT | Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon. | Zihan Zhang, Xiangyang Ji, Simon S. Du |
| 2021 | ICML | Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. | Yifang Chen, Simon S. Du, Kevin Jamieson |
| 2021 | ICML | Bilinear Classes: A Structural Framework for Provable Generalization in RL. | Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang |
| 2021 | ICML | On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP. | Tianhao Wu, Yunchang Yang, Simon S. Du, Liwei Wang |
| 2021 | ICML | Near Optimal Reward-Free Reinforcement Learning. | Zihan Zhang, Simon S. Du, Xiangyang Ji |
| 2021 | UAI | 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 |
| 2020 | ICLR | 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 |
| 2020 | ICLR | Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? | Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang |
| 2020 | ICLR | What Can Neural Networks Reason About? | Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka |
| 2020 | ICML | Provable Representation Learning for Imitation Learning via Bi-level Optimization. | Sanjeev Arora, Simon S. Du, Sham M. Kakade, Yuping Luo, Nikunj Saunshi |
| 2020 | IJCAI | 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 |
| 2019 | AISTATS | Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity. | Simon S. Du, Wei Hu |
| 2019 | ICLR | Gradient Descent Provably Optimizes Over-parameterized Neural Networks. | Simon S. Du, Xiyu Zhai, Barnabs Pczos, Aarti Singh |
| 2019 | ICML | Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. | Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang |
| 2019 | ICML | Width Provably Matters in Optimization for Deep Linear Neural Networks. | Simon S. Du, Wei Hu |
| 2019 | ICML | Provably efficient RL with Rich Observations via Latent State Decoding. | Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudk, John Langford |
| 2019 | ICML | Gradient Descent Finds Global Minima of Deep Neural Networks. | Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai |
| 2018 | AISTATS | Stochastic Zeroth-order Optimization in High Dimensions. | Yining Wang, Simon S. Du, Sivaraman Balakrishnan, Aarti Singh |
| 2018 | ICLR | When is a Convolutional Filter Easy to Learn? | Simon S. Du, Jason D. Lee, Yuandong Tian |
| 2018 | ICML | On the Power of Over-parametrization in Neural Networks with Quadratic Activation. | Simon S. Du, Jason D. Lee |
| 2018 | ICML | Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. | Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabs Pczos |
| 2018 | ICML | Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms. | Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon S. Du, Stuart Russell |
| 2018 | ICML | Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. | Xiao Zhang, Simon S. Du, Quanquan Gu |
| 2017 | COLT | Computationally Efficient Robust Sparse Estimation in High Dimensions. | Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh |
| 2017 | ICML | Stochastic Variance Reduction Methods for Policy Evaluation. | Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou |
| 2015 | AISTATS | Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrm Method. | David G. Anderson, Simon S. Du, Michael W. Mahoney, Christopher Melgaard, Kunming Wu, Ming Gu |