I am a postdoctoral researcher at Microsoft Research in New York City. I study machine learning for sequential decision-making. I am a Ph.D. candidate at Princeton University, advised by Chi Jin. My PhD research focused on partially observable RL (POMDP, PSR, Survey), multi-agent RL (Stochastic Game), and RL with large state spaces (Function Approximation). During summer 2022, I interned at DeepMind, working with Csaba Szepesvári and Gellért Weisz. Previously, I received a B.E. degree in Electrical Engineering and a B.S. degree in Mathematics from Tsinghua University. I will be joining WashU CS as an assistant professor in the fall of 2025. |
(α-β order) denotes alphabetical authorship ordering
Optimistic MLE – A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
Symposium on Theory of Computing (STOC), 2023
V-Learning – A Simple, Efficient, Decentralized Algorithm for Multiagent RL
(α-β order) Chi Jin, Qinghua Liu, Yuanhao Wang, Tiancheng Yu
Mathematics of Operations Research (MOR), 2023
Best Paper in ICLR 2022 ‘‘Gamification and Multiagent Solutions’’ Workshop
When Is Partially Observable Reinforcement Learning Not Scary?
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
Conference on Learning Theory (COLT), 2022
Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms
(α-β order) Chi Jin, Qinghua Liu, Sobhan Miryoosefi
Neural Information Processing Systems (NeurIPS), 2021 (Spotlight)