Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

Picture for Chi Jin

A Simple Reward-free Approach to Constrained Reinforcement Learning


Jul 12, 2021
Sobhan Miryoosefi, Chi Jin


  Access Paper or Ask Questions

The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces


Jun 07, 2021
Chi Jin, Qinghua Liu, Tiancheng Yu


  Access Paper or Ask Questions

Minimax Optimization with Smooth Algorithmic Adversaries


Jun 02, 2021
Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff


  Access Paper or Ask Questions

An almost globally convergent observer for visual SLAM without persistent excitation


Apr 07, 2021
Bowen Yi, Chi Jin, Lei Wang, Guodong Shi, Ian R. Manchester


  Access Paper or Ask Questions

Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning


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


  Access Paper or Ask Questions

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


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


  Access Paper or Ask Questions

Near-optimal Representation Learning for Linear Bandits and Linear RL


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


  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

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


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


  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

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


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


  Access Paper or Ask Questions

Near-Optimal Reinforcement Learning with Self-Play


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


  Access Paper or Ask Questions

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs


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


  Access Paper or Ask Questions

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


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


  Access Paper or Ask Questions

Provable Meta-Learning of Linear Representations


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


  Access Paper or Ask Questions

Provable Self-Play Algorithms for Competitive Reinforcement Learning


Feb 23, 2020
Yu Bai, Chi Jin


  Access Paper or Ask Questions

Reward-Free Exploration for Reinforcement Learning


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


  Access Paper or Ask Questions

Near-Optimal Algorithms for Minimax Optimization


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

* 40 pages 

  Access Paper or Ask Questions

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 

  Access Paper or Ask Questions

Provably Efficient Exploration in Policy Optimization


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


  Access Paper or Ask Questions

Provably Efficient Reinforcement Learning with Linear Function Approximation


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


  Access Paper or Ask Questions

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems


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


  Access Paper or Ask Questions

Stochastic Gradient Descent Escapes Saddle Points Efficiently


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


  Access Paper or Ask Questions

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


  Access Paper or Ask Questions

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


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


  Access Paper or Ask Questions

Sampling Can Be Faster Than Optimization


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


  Access Paper or Ask Questions