(α-β order) denotes alphabetical ordering, * denotes equal contribution.
Is Elo Rating Reliable? A Study Under Model Misspecification [arXiv]
Shange Tang, Yuanhao Wang, Chi Jin
ArXiv Preprint
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games [arXiv]
Zihan Ding, Dijia Su, Qinghua Liu, Chi Jin
ArXiv Preprint
Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games [arXiv]
Jiawei Ge*, Yuanhao Wang*, Wenzhe Li, Chi Jin
International Conference on Machine Learning (ICML) 2025
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning [arXiv]
Wenzhe Li, Zihan Ding, Seth Karten, Chi Jin
International Conference on Machine Learning (ICML) 2024
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”
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation [arXiv]
Yuanhao Wang*, Qinghua Liu*, Yu Bai+, Chi Jin+
Conference of Learning Theory (COLT) 2023
Learning Rationalizable Equilibria in Multiplayer Games [arXiv]
Yuanhao Wang*, Dingwen Kong*, Yu Bai, Chi Jin
International Conference on Learning Representations (ICLR) 2023
Representation Learning for Low-rank General-sum Markov Games [arXiv]
Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang
International Conference on Learning Representations (ICLR) 2023.
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games [arXiv]
Qinghua Liu, Csaba Szepesvári, Chi Jin
Neural Information Processing Systems (NIPS) 2022.
Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent [arXiv]
(α-β order) Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu
Neural Information Processing Systems (NIPS) 2022.
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits [arXiv]
Qinghua Liu, Yuanhao Wang, Chi Jin
International Conference on Machine Learning (ICML) 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.
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces [arXiv]
(α-β order) Chi Jin, Qinghua Liu, Tiancheng Yu
International Conference on Machine Learning (ICML) 2022.
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games [arXiv]
Yu Bai, Chi Jin, Huan Wang, Caiming Xiong
Neural Information Processing Systems (NIPS) 2021.
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play [arXiv]
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
International Conference on Machine Learning (ICML) 2021.
Near-Optimal Reinforcement Learning with Self-Play [arXiv]
(α-β order) Yu Bai, Chi Jin, Tiancheng Yu
Neural Information Processing Systems (NIPS) 2020.
Provable Self-Play Algorithms for Competitive Reinforcement Learning [arXiv]
(α-β order) Yu Bai, Chi Jin
International Conference on Machine Learning (ICML) 2020.