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Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret


Apr 22, 2021
Jean Tarbouriech, Runlong Zhou, Simon S. Du, Matteo Pirotta, Michal Valko, Alessandro Lazaric


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Nearly Horizon-Free Offline Reinforcement Learning


Mar 25, 2021
Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi


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Bilinear Classes: A Structural Framework for Provable Generalization in RL


Mar 19, 2021
Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang


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Improved Corruption Robust Algorithms for Episodic Reinforcement Learning


Mar 08, 2021
Yifang Chen, Simon S. Du, Kevin Jamieson


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Variance-Aware Confidence Set: Variance-Dependent Bound for Linear Bandits and Horizon-Free Bound for Linear Mixture MDP


Feb 19, 2021
Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du

* 31 pages 

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Randomized Exploration is Near-Optimal for Tabular MDP


Feb 19, 2021
Zhihan Xiong, Ruoqi Shen, Simon S. Du


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Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games


Feb 17, 2021
Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du


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Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap


Feb 09, 2021
Haike Xu, Tengyu Ma, Simon S. Du


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A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost


Jan 02, 2021
Minbo Gao, Tianle Xie, Simon S. Du, Lin F. Yang


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Nearly Minimax Optimal Reward-free Reinforcement Learning


Oct 23, 2020
Zihan Zhang, Simon S. Du, Xiangyang Ji


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Provable Benefits of Representation Learning in Linear Bandits


Oct 13, 2020
Jiaqi Yang, Wei Hu, Jason D. Lee, Simon S. Du

* 28 pages, 6 figures 

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How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks


Oct 01, 2020
Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka


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Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon


Sep 28, 2020
Zihan Zhang, Xiangyang Ji, Simon S. Du


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On Reward-Free Reinforcement Learning with Linear Function Approximation


Jun 19, 2020
Ruosong Wang, Simon S. Du, Lin F. Yang, Ruslan Salakhutdinov


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$Q$-learning with Logarithmic Regret


Jun 16, 2020
Kunhe Yang, Lin F. Yang, Simon S. Du


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When is Particle Filtering Efficient for POMDP Sequential Planning?


Jun 10, 2020
Simon S. Du, Wei Hu, Zhiyuan Li, Ruoqi Shen, Zhao Song, Jiajun Wu


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Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?


May 01, 2020
Ruosong Wang, Simon S. Du, Lin F. Yang, Sham M. Kakade


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Provably Efficient Exploration for RL with Unsupervised Learning


Mar 15, 2020
Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin F. Yang


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Provable Representation Learning for Imitation Learning via Bi-level Optimization


Feb 24, 2020
Sanjeev Arora, Simon S. Du, Sham Kakade, Yuping Luo, Nikunj Saunshi

* 26 pages 

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Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality


Feb 24, 2020
Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora


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Few-Shot Learning via Learning the Representation, Provably


Feb 21, 2020
Simon S. Du, Wei Hu, Sham M. Kakade, Jason D. Lee, Qi Lei


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Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity


Feb 17, 2020
Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang


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Optimism in Reinforcement Learning with Generalized Linear Function Approximation


Dec 09, 2019
Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy


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Enhanced Convolutional Neural Tangent Kernels


Nov 03, 2019
Zhiyuan Li, Ruosong Wang, Dingli Yu, Simon S. Du, Wei Hu, Ruslan Salakhutdinov, Sanjeev Arora


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Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?


Nov 03, 2019
Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang


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Continuous Control with Contexts, Provably


Oct 30, 2019
Simon S. Du, Ruosong Wang, Mengdi Wang, Lin F. Yang


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