Chi Jin (金驰)

Assistant Professor of Electrical and Computer Engineering

Associated Faculty Member of Computer Science

Princeton University

Email: chij (at) princeton (dot) edu

[Curriculum Vitae]

[Google Scholar]

I am currently an assistant professor of Electrical Engineering at Princeton University. I obtained my Ph.D. in Computer Science at UC Berkeley, advised by Michael I. Jordan. Prior to that, I received a B.S. in Physics from Peking University, and did my undergraduate thesis with Liwei Wang.

My research interests lie in machine learning theory, statistics, optimization and game theory. His research aim to develop principal and theoretical sound methodology for modern machine learning. My past research has mainly focuses on nonconvex optimization and Reinforcement Learning (RL). In nonconvex optimization, I provided the first proof showing that first-order algorithm (stochastic gradient descent) is capable of escaping saddle points efficiently. In RL, he provided the first efficient learning guarantees for Q-learning and least-squares value iteration algorithms when exploration is necessary. My works also establish the theoretical foundation for RL with function approximation, multiagent RL and partially observable RL.

Selected Paper

When Is Partially Observable Reinforcement Learning Not Scary? [arXiv]


Near-Optimal Learning of Extensive-Form Games with Imperfect Information. [arXiv]


V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL [arXiv]


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


Near-Optimal Algorithms for Minimax Optimization [arXiv]


Provably Efficient Reinforcement Learning with Linear Function Approximation [arXiv]


Is Q-learning Provably Efficient? [arXiv]


Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent [arXiv]


How to Escape Saddle Points Efficiently [arXiv] [blog]

Education

Experience

Former Students

Former Visitors