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Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning


Mar 25, 2021
Yaqi Duan, Chi Jin, Zhiyuan Li


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Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games


Feb 23, 2021
Yu Bai, Chi Jin, Huan Wang, Caiming Xiong


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Near-optimal Representation Learning for Linear Bandits and Linear RL


Feb 08, 2021
Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang


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Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms


Feb 05, 2021
Chi Jin, Qinghua Liu, Sobhan Miryoosefi

* We propose a new complexity measure and an optimization-based sample-efficient algorithm for reinforcement learning with function approximation 

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A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network


Feb 04, 2021
Mo Zhou, Rong Ge, Chi Jin


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Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations


Nov 09, 2020
Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan

* 76 pages. The short version of this work appears in NeurIPS 2020 

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A Sharp Analysis of Model-based Reinforcement Learning with Self-Play


Oct 04, 2020
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin


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Near-Optimal Reinforcement Learning with Self-Play


Jul 14, 2020
Yu Bai, Chi Jin, Tiancheng Yu


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Sample-Efficient Reinforcement Learning of Undercomplete POMDPs


Jun 22, 2020
Chi Jin, Sham M. Kakade, Akshay Krishnamurthy, Qinghua Liu


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On the Theory of Transfer Learning: The Importance of Task Diversity


Jun 20, 2020
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin


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Provable Meta-Learning of Linear Representations


Feb 26, 2020
Nilesh Tripuraneni, Chi Jin, Michael I. Jordan


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Provable Self-Play Algorithms for Competitive Reinforcement Learning


Feb 23, 2020
Yu Bai, Chi Jin


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Reward-Free Exploration for Reinforcement Learning


Feb 07, 2020
Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu


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Near-Optimal Algorithms for Minimax Optimization


Feb 05, 2020
Tianyi Lin, Chi Jin, Michael. I. Jordan

* 40 pages 

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Learning Adversarial MDPs with Bandit Feedback and Unknown Transition


Jan 07, 2020
Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu

* Improved the algorithm with a tighter confidence set 

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Provably Efficient Exploration in Policy Optimization


Dec 12, 2019
Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang


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Provably Efficient Reinforcement Learning with Linear Function Approximation


Aug 08, 2019
Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan


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On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems


Jun 02, 2019
Tianyi Lin, Chi Jin, Michael I. Jordan


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Stochastic Gradient Descent Escapes Saddle Points Efficiently


Feb 13, 2019
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan


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A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm


Feb 11, 2019
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan


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Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal


Feb 02, 2019
Chi Jin, Praneeth Netrapalli, Michael I. Jordan


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Sampling Can Be Faster Than Optimization


Nov 20, 2018
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, Michael I. Jordan


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On the Local Minima of the Empirical Risk


Oct 17, 2018
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan

* To appear in NIPS 2018 

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Is Q-learning Provably Efficient?


Jul 10, 2018
Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael I. Jordan

* Best paper in ICML 2018 workshop "Exploration in RL" 

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Stability and Convergence Trade-off of Iterative Optimization Algorithms


Apr 04, 2018
Yuansi Chen, Chi Jin, Bin Yu

* 45 pages, 7 figures 

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Stochastic Cubic Regularization for Fast Nonconvex Optimization


Dec 05, 2017
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan

* The first two authors contributed equally 

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