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Tiancheng Yu's Homepage
Tiancheng Yu
Tiancheng Yu (余天呈)
yutc [at] mit [dot] edu
I am a researcher at OpenAI.
I completed my Ph.D. in 2023 and M.S. in 2020, both at MIT EECS and advised by Prof. Suvrit Sra. I worked on provably robust reinforcement learning (RL) and near-optimal learning in sequential games. I completed my B.E. in 2018 at Tsinghua University, major in Electronic Engineering and minor in Statistics. I was fortunate to be advised by Prof. Yuan Shen.
In Fall 2019, I visited Princeton Univerisity and worked with Prof. Chi Jin. In Summer 2017, I was fortunate to join Stanford UGVR program and advised by Prof. Tsachy Weissman. In Fall 2016, I was an exchange student in University of Wisconsin, Madison and worked with Prof. Dimitris Papailiopoulos.
Selected Publications
*: equal contribution or alphabetical order.- Mingyang Liu*, Asuman Ozdaglar*, Tiancheng Yu* and Kaiqing Zhang*. The Power of Regularization in Solving Extensive-Form Games. ICLR 2023.
- Yu Bai*, Chi Jin*, Song Mei*, Ziang Song* and Tiancheng Yu*. Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. NeurIPS 2022 Oral.
- Yu Bai*, Chi Jin*, Song Mei* and Tiancheng Yu*. Near-Optimal Learning of Extensive-Form Games with Imperfect Information. ICML 2022. Preliminary version presented as contributed talk at the ICLR 2022 Workshop on Gamification and Multiagent Solutions.
- Chi Jin*, Qinghua Liu*, Yuanhao Wang* and Tiancheng Yu*. V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL. Mathematics of Operations Research. Preliminary version selected as Best Paper at the ICLR 2022 Workshop on Gamification and Multiagent Solutions.
- Chi Jin*, Qinghua Liu* and Tiancheng Yu*. The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces. ICML 2022. Preliminary version presented on ICML 2021 Workshop on Reinforcement Learning Theory.
- Tiancheng Yu, Yi Tian, Jingzhao Zhang and Suvrit Sra. Provably Efficient Algorithms for Multi-Objective Competitive RL. ICML 2021 Long Presentation (Top 3%).
- Yi Tian*, Yuanhao Wang*, Tiancheng Yu* and Suvrit Sra. Online Learning in Unknown Markov Games. ICML 2021.
- Qinghua Liu, Tiancheng Yu, Yu Bai and Chi Jin. A Sharp Analysis of Model-based Reinforcement Learning with Self-Play. ICML 2021.
- Chi-Ning Chou*, Juspreet Singh Sandhu*, Mien Brabeeba Wang* and Tiancheng Yu*. A General Framework for Analyzing Stochastic Dynamics in Learning Algorithms. Submitted.
- Yu Bai*, Chi Jin* and Tiancheng Yu*. Near-Optimal Reinforcement Learning with Self-Play. NeurIPS 2020. Preliminary version presented on ICML 2020 Theoretical Foundations of RL workshop. Short talk (Top 6%).
- Chi Jin*, Akshay Krishnamurthy*, Max Simchowitz* and Tiancheng Yu*. Reward-Free Exploration for Reinforcement Learning. ICML 2020.
- Chi Jin*, Tiancheng Jin*, Haipeng Luo*, Suvrit Sra* and Tiancheng Yu*. Learning Adversarial MDPs with Bandit Feedback and Unknown Transition. ICML 2020.
- Yanjun Han*, Jiantao Jiao*, Chuan-Zheng Lee*, Tsachy Weissman*, Yihong Wu* and Tiancheng Yu*. Entropy Rate Estimation for Markov Chains with Large State Space. NIPS 2018.
Miscellaneous
- I enjoy piano and vocal (Bel-Canto) performing in my spare time. I am fortunate to learn from the inspiring musicians Tianxu An, Xiaopei Xu, Jen-tao Yu, Bingchuan Wan, Yongqian zhang (张永前) and Linbo Fu (付林波).
- I used to do competitive bodybuilding and join the Varsity Team in Tsinghua in 2016. Later I gradually switch to powerlifting. I am fortunate to learn from the fantastic athletes and coaches Max Dremel, Dan Green, Cheng Tang (唐诚) and Chenyu Mao (毛晨雨).
- I translated the cute AI book "You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place " into Chinese:《你看起来好像……我爱你(AI的工作原理以及它为这个世界带来的稀奇古怪)》(Janelle Shane著, 中信出版集团）. Check it out!
- © Tiancheng Yu 2017