Research Interests
LLM Reasoning & Agents
Game Theory & Multiagent Learning
Reinforcement Learning
Statistical Learning Theory
Optimization
Education
Ph.D. in Computer Science, 2013 - 2019
University of California, Berkeley.
Advisor: Michael I. Jordan
BSc in Physics, 2008 - 2012
Peking University.
My research focuses on the decision-making aspects of machine learning. We aims to develop intelligent agents capable of complex strategy, advanced reasoning, and planning. In the past, my group has primarily worked on establishing the theoretical foundations of machine learning, including areas such as reinforcement learning, multi-agent learning, game theory, statistical learning, and optimization. Recently, we have expanded our interests to include improving the reasoning abilities of LLM and developing LLM agents for tasks such as mathematics, coding, and complex games.
I have been recently giving talks on beyond equilibrium learning in game theory. See also my Princeton course on foundations of reinforcement learning here, and my tutorial on multiagent reinforcement learning at Simons institute here.
*I am recruiting undergraduate research interns, PhD students, and Postdoctoral researchers*. To apply, please email me with your CV attached. PhD applicants should also mention my name as a faculty member of interest in their statement of purpose. Due to the high volume of inquiries, I may not be able to respond until the hiring process begins.
Awards
Sloan Research Fellowship, 2024
E. Lawrence Keyes, Jr./Emerson Electric Co. Faculty Advancement Award, 2023
NSF CAREER Award, Division of Information and Intelligent Systems, 2023
Princeton Commendation for Outstanding Teaching for ECE 524, 2022 & 2024
Princeton Commendation for Outstanding Teaching for ECE 539, 2021 & 2023
Princeton School of Engineering and Applied Science (SEAS) Innovation Award, 2022
Best Paper Award in ICLR 2022 workshop “Gamification and Multiagent Solutions”.
Best Paper Award in ICML 2018 workshop “Exploration in Reinforcement Learning”.
Selected Paper
Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games [arXiv]
Jiawei Ge*, Yuanhao Wang*, Wenzhe Li, Chi Jin
ArXiv Preprint
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift [arXiv]
Jiawei Ge*, Shange Tang*, Jianqing Fan, Cong Ma, Chi Jin
International Conference on Learning Representations (ICLR) 2024
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making [arXiv]
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvari, Chi Jin
Symposium on Theory of Computing (STOC) 2023
When Is Partially Observable Reinforcement Learning Not Scary? [arXiv]
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
Conference of Learning Theory (COLT) 2022.
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. [arXiv]
(α-β order) Yu Bai, Chi Jin, Song Mei, Tiancheng Yu
International Conference on Machine Learning (ICML) 2022.
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL [arXiv]
(α-β order) Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu
Mathematics of Operation Research (MOR) 2023
Best Paper in ICLR 2022 workshop “Gamification and Multiagent Solutions”
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms [arXiv]
(α-β order) Chi Jin, Qinghua Liu, Sobhan Miryoosefi
Neural Information Processing Systems (NIPS) 2021.
Near-Optimal Algorithms for Minimax Optimization [arXiv]
Tianyi Lin, Chi Jin, Michael. I. Jordan
Conference of Learning Theory (COLT) 2020
Provably Efficient Reinforcement Learning with Linear Function Approximation [arXiv]
Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan
Conference of Learning Theory (COLT) 2020
Is Q-learning Provably Efficient? [arXiv]
Chi Jin*, Zeyuan Allen-Zhu*, Sebastien Bubeck, Michael I. Jordan
Neural Information Processing Systems (NIPS) 2018.
Best Paper in ICML 2018 workshop "Exploration in RL"
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent [arXiv]
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Conference of Learning Theory (COLT) 2018
How to Escape Saddle Points Efficiently [arXiv] [blog]
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
International Conference on Machine Learning (ICML) 2017.