Simon S. Du

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 196

Top Venues

CoRR

NeurIPS

ICML

COLT

Publications

Chuning Zhu, Xinqi Wang, Tyler Han, Simon S. Du, Abhishek Gupta
Transferable Reinforcement Learning via Generalized Occupancy Models. CoRR (2024)
Chenhao Lu, Ruizhe Shi, Yuyao Liu, Kaizhe Hu, Simon S. Du, Huazhe Xu
Rethinking Transformers in Solving POMDPs. CoRR (2024)
Yan Dai, Qiwen Cui, Simon S. Du
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract). COLT (2024)
Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee
Optimal Multi-Distribution Learning. COLT (2024)
Runlong Zhou, Simon S. Du, Beibin Li
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs. ACL (1) (2024)
Ruizhe Shi, Yifang Chen, Yushi Hu, Alisa Liu, Hannaneh Hajishirzi, Noah A. Smith, Simon S. Du
Decoding-Time Language Model Alignment with Multiple Objectives. CoRR (2024)
Qiwen Cui, Maryam Fazel, Simon S. Du
Learning Optimal Tax Design in Nonatomic Congestion Games. CoRR (2024)
Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du
Settling the sample complexity of online reinforcement learning. COLT (2024)
Runlong Zhou, Simon S. Du, Beibin Li
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs. CoRR (2024)
Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. CoRR (2024)
Weihang Xu, Maryam Fazel, Simon S. Du
Toward Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixture Models. CoRR (2024)
Divyansh Pareek, Simon S. Du, Sewoong Oh
Understanding the Gains from Repeated Self-Distillation. CoRR (2024)
Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon S. Du
Horizon-Free Regret for Linear Markov Decision Processes. CoRR (2024)
Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. ACL (Findings) (2024)
Yan Dai, Qiwen Cui, Simon S. Du
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation. CoRR (2024)
Yunchang Yang, Han Zhong, Tianhao Wu, Bin Liu, Liwei Wang, Simon S. Du
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback. NeurIPS (2023)
Qiwen Cui, Kaiqing Zhang, Simon S. Du
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation. CoRR (2023)
Yulai Zhao, Jianshu Chen, Simon S. Du
Blessing of Class Diversity in Pre-training. AISTATS (2023)
Weihang Xu, Simon S. Du
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron. COLT (2023)
Ruizhe Shi, Yuyao Liu, Yanjie Ze, Simon S. Du, Huazhe Xu
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning. CoRR (2023)
Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. NeurIPS (2023)
Yuandong Tian, Yiping Wang, Zhenyu Zhang, Beidi Chen, Simon S. Du
JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention. CoRR (2023)
Nuoya Xiong, Lijun Ding, Simon S. Du
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization. CoRR (2023)
Haozhe Jiang, Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning. CoRR (2023)
Yuandong Tian, Yiping Wang, Beidi Chen, Simon S. Du
Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer. CoRR (2023)
Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon S. Du, Jason D. Lee
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. CoRR (2023)
Weihang Xu, Simon S. Du
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron. CoRR (2023)
Runlong Zhou, Zihan Zhang, Simon S. Du
Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments. CoRR (2023)
Yifang Chen, Yingbing Huang, Simon S. Du, Kevin G. Jamieson, Guanya Shi
Active Representation Learning for General Task Space with Applications in Robotics. CoRR (2023)
Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee
Optimal Multi-Distribution Learning. CoRR (2023)
Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon S. Du, Jason D. Lee, Wei Hu
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. CoRR (2023)
Qiwen Cui, Kaiqing Zhang, Simon S. Du
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation. COLT (2023)
Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du
Settling the Sample Complexity of Online Reinforcement Learning. CoRR (2023)
Yunchang Yang, Han Zhong, Tianhao Wu, Bin Liu, Liwei Wang, Simon S. Du
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback. CoRR (2023)
Yuandong Tian, Yiping Wang, Beidi Chen, Simon S. Du
Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer. NeurIPS (2023)
Yifang Chen, Yingbing Huang, Simon S. Du, Kevin G. Jamieson, Guanya Shi
Active representation learning for general task space with applications in robotics. NeurIPS (2023)
Yiping Wang, Yifang Chen, Kevin G. Jamieson, Simon S. Du
Improved Active Multi-Task Representation Learning via Lasso. CoRR (2023)
Qiwen Cui, Simon S. Du
When are Offline Two-Player Zero-Sum Markov Games Solvable? NeurIPS (2022)
Zhihan Xiong, Ruoqi Shen, Qiwen Cui, Maryam Fazel, Simon S. Du
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes. NeurIPS (2022)
Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. AISTATS (2022)
Rui Yuan, Simon S. Du, Robert M. Gower, Alessandro Lazaric, Lin Xiao
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. CoRR (2022)
Qiwen Cui, Simon S. Du
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus. NeurIPS (2022)
Qiwen Cui, Simon S. Du
When is Offline Two-Player Zero-Sum Markov Game Solvable? CoRR (2022)
Xinqi Wang, Qiwen Cui, Simon S. Du
On Gap-dependent Bounds for Offline Reinforcement Learning. NeurIPS (2022)
Zihan Zhang, Xiangyang Ji, Simon S. Du
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies. COLT (2022)
Yifang Chen, Kevin G. Jamieson, Simon S. Du
Active Multi-Task Representation Learning. ICML (2022)
Haozhe Jiang, Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du
Offline congestion games: How feedback type affects data coverage requirement. CoRR (2022)
Runlong Zhou, Ruosong Wang, Simon S. Du
Horizon-Free Reinforcement Learning for Latent Markov Decision Processes. CoRR (2022)
Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian
Denoised MDPs: Learning World Models Better Than the World Itself. CoRR (2022)
Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. ICML (2022)
Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon S. Du
Gap-Dependent Bounds for Two-Player Markov Games. AISTATS (2022)
Xinqi Wang, Qiwen Cui, Simon S. Du
On Gap-dependent Bounds for Offline Reinforcement Learning. CoRR (2022)
Yan Dai, Ruosong Wang, Simon S. Du
Variance-Aware Sparse Linear Bandits. CoRR (2022)
Rui Lu, Andrew Zhao, Simon S. Du, Gao Huang
Provable General Function Class Representation Learning in Multitask Bandits and MDPs. CoRR (2022)
Bin Shi, Simon S. Du, Michael I. Jordan, Weijie J. Su
Understanding the acceleration phenomenon via high-resolution differential equations. Math. Program. 195 (1) (2022)
Jiaqi Yang, Qi Lei, Jason D. Lee, Simon S. Du
Nearly Minimax Algorithms for Linear Bandits with Shared Representation. CoRR (2022)
Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du
Learning in Congestion Games with Bandit Feedback. CoRR (2022)
Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. CoRR (2022)
Haoyuan Cai, Tengyu Ma, Simon S. Du
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path. CoRR (2022)
Simon S. Du, Gauthier Gidel, Michael I. Jordan, Chris Junchi Li
Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. CoRR (2022)
Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du
Learning in Congestion Games with Bandit Feedback. NeurIPS (2022)
Yifang Chen, Simon S. Du, Kevin Jamieson
Active Multi-Task Representation Learning. CoRR (2022)
Haotian Ye, Xiaoyu Chen, Liwei Wang, Simon S. Du
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness. CoRR (2022)
Andrew J. Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin G. Jamieson
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. ICML (2022)
Yulai Zhao, Jianshu Chen, Simon S. Du
Blessing of Class Diversity in Pre-training. CoRR (2022)
Shicong Cen, Yuejie Chi, Simon S. Du, Lin Xiao
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games. CoRR (2022)
Haoyuan Cai, Tengyu Ma, Simon S. Du
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path. ICML (2022)
Rui Lu, Andrew Zhao, Simon S. Du, Gao Huang
Provable General Function Class Representation Learning in Multitask Bandits and MDP. NeurIPS (2022)
Qiwen Cui, Simon S. Du
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus. CoRR (2022)
Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. ICML (2022)
Meixin Zhu, Simon S. Du, Xuesong Wang, Hao Yang, Ziyuan Pu, Yinhai Wang
TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer. CoRR (2022)
Zihan Zhang, Xiangyang Ji, Simon S. Du
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies. CoRR (2022)
Runlong Zhou, Yuandong Tian, Yi Wu, Simon S. Du
Understanding Curriculum Learning in Policy Optimization for Solving Combinatorial Optimization Problems. CoRR (2022)
Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon S. Du
Gap-Dependent Bounds for Two-Player Markov Games. CoRR (2021)
Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon S. Du
A Unified Framework for Conservative Exploration. CoRR (2021)
Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP. NeurIPS (2021)
Haike Xu, Tengyu Ma, Simon S. Du
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap. CoRR (2021)
Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du
Variance-Aware Confidence Set: Variance-Dependent Bound for Linear Bandits and Horizon-Free Bound for Linear Mixture MDP. CoRR (2021)
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Bilinear Classes: A Structural Framework for Provable Generalization in RL. ICML (2021)
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Bilinear Classes: A Structural Framework for Provable Generalization in RL. CoRR (2021)
Yifang Chen, Simon S. Du, Kevin Jamieson
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. ICML (2021)
Yining Wang, Yi Wu, Simon S. Du
Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels. INFORMS J. Comput. 33 (4) (2021)
Xiang Wang, Xinlei Chen, Simon S. Du, Yuandong Tian
Towards Demystifying Representation Learning with Non-contrastive Self-supervision. CoRR (2021)
Andrew Wagenmaker, Yifang Chen, Max Simchowitz, Simon S. Du, Kevin Jamieson
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. CoRR (2021)
Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi
Nearly Horizon-Free Offline Reinforcement Learning. CoRR (2021)
Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon S. Du, Jiantao Jiao
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. CoRR (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. CoRR (2021)
Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon S. Du, Yu Wang, Yi Wu
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization. CoRR (2021)
Zihan Zhang, Simon S. Du, Xiangyang Ji
Near Optimal Reward-Free Reinforcement Learning. ICML (2021)
Rui Lu, Gao Huang, Simon S. Du
On the Power of Multitask Representation Learning in Linear MDP. CoRR (2021)
Zhili Feng, Shaobo Han, Simon S. Du
Provable Adaptation across Multiway Domains via Representation Learning. CoRR (2021)
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. NeurIPS (2021)
Tian Ye, Simon S. Du
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization. CoRR (2021)
Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi
Nearly Horizon-Free Offline Reinforcement Learning. NeurIPS (2021)
Minbo Gao, Tianle Xie, Simon S. Du, Lin F. Yang
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost. CoRR (2021)
Zhihan Xiong, Ruoqi Shen, Simon S. Du
Randomized Exploration is Near-Optimal for Tabular MDP. CoRR (2021)
Tian Ye, Simon S. Du
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization. NeurIPS (2021)
Simon S. Du, Wei Hu, Zhiyuan Li, Ruoqi Shen, Zhao Song, Jiajun Wu
When is particle filtering efficient for planning in partially observed linear dynamical systems? UAI (2021)
Xiaoxia Wu, Yuege Xie, Simon S. Du, Rachel Ward
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method. CoRR (2021)
Yifang Chen, Simon S. Du, Kevin Jamieson
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. CoRR (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Simon S. Du