| 2026 | COLT | Regret Minimization with Adaptive Opponents in Repeated Games. | Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu, Kaiqing Zhang |
| 2023 | ICLR | The Power of Regularization in Solving Extensive-Form Games. | Mingyang Liu, Asuman E. Ozdaglar, Tiancheng Yu, Kaiqing Zhang |
| 2022 | ICML | Near-Optimal Learning of Extensive-Form Games with Imperfect Information. | Yu Bai, Chi Jin, Song Mei, Tiancheng Yu |
| 2022 | ICML | The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. | Chi Jin, Qinghua Liu, Tiancheng Yu |
| 2021 | ICML | A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. | Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin |
| 2021 | ICML | Online Learning in Unknown Markov Games. | Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra |
| 2021 | ICML | Provably Efficient Algorithms for Multi-Objective Competitive RL. | Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra |
| 2020 | ICML | Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition. | Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu |
| 2020 | ICML | Reward-Free Exploration for Reinforcement Learning. | Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu |
| 2018 | GLOBECOM | A Probabilistic Learning Approach to UWB Ranging Error Mitigation. | Chengzhi Mao, Kangbo Lin, Tiancheng Yu, Yuan Shen |