| Reward-free exploration for reinforcement learning C Jin, A Krishnamurthy, M Simchowitz, T Yu International Conference on Machine Learning, 4870-4879, 2020 | 265 | 2020 |
| Near-optimal reinforcement learning with self-play Y Bai, C Jin, T Yu Advances in neural information processing systems 33, 2159-2170, 2020 | 164 | 2020 |
| A sharp analysis of model-based reinforcement learning with self-play Q Liu, T Yu, Y Bai, C Jin International Conference on Machine Learning, 7001-7010, 2021 | 159 | 2021 |
| Learning adversarial markov decision processes with bandit feedback and unknown transition C Jin, T Jin, H Luo, S Sra, T Yu International Conference on Machine Learning, 4860-4869, 2020 | 154* | 2020 |
| V-Learning--A Simple, Efficient, Decentralized Algorithm for Multiagent RL C Jin, Q Liu, Y Wang, T Yu arXiv preprint arXiv:2110.14555, 2021 | 122 | 2021 |
| The power of exploiter: Provable multi-agent rl in large state spaces C Jin, Q Liu, T Yu International Conference on Machine Learning, 10251-10279, 2022 | 71 | 2022 |
| Online learning in unknown markov games Y Tian, Y Wang, T Yu, S Sra International conference on machine learning, 10279-10288, 2021 | 58 | 2021 |
| Near-optimal learning of extensive-form games with imperfect information Y Bai, C Jin, S Mei, T Yu International Conference on Machine Learning, 1337-1382, 2022 | 33 | 2022 |
| A probabilistic learning approach to UWB ranging error mitigation C Mao, K Lin, T Yu, Y Shen 2018 IEEE Global Communications Conference (GLOBECOM), 1-6, 2018 | 31 | 2018 |
| Provably efficient algorithms for multi-objective competitive rl T Yu, Y Tian, J Zhang, S Sra International Conference on Machine Learning, 12167-12176, 2021 | 27 | 2021 |
| Efficient Phi-regret minimization in extensive-form games via online mirror descent Y Bai, C Jin, S Mei, Z Song, T Yu Advances in Neural Information Processing Systems 35, 22313-22325, 2022 | 23 | 2022 |
| Entropy rate estimation for Markov chains with large state space Y Han, J Jiao, CZ Lee, T Weissman, Y Wu, T Yu Advances in Neural Information Processing Systems 31, 2018 | 23 | 2018 |
| The power of regularization in solving extensive-form games M Liu, A Ozdaglar, T Yu, K Zhang arXiv preprint arXiv:2206.09495, 2022 | 22 | 2022 |
| Provably efficient online agnostic learning in Markov games Y Tian, Y Wang, T Yu, S Sra arXiv preprint arXiv:2010.15020, 2020 | 21 | 2020 |
| V-learning—a simple, efficient, decentralized algorithm for multiagent reinforcement learning C Jin, Q Liu, Y Wang, T Yu Mathematics of Operations Research 49 (4), 2295-2322, 2024 | 7 | 2024 |
| Asymptotic performance analysis for landmark learning in indoor localization T Yu, Y Shen IEEE Communications Letters 22 (4), 740-743, 2018 | 7 | 2018 |
| Towards efficient evaluation of risk via herding Z Xu, T Yu, S Sra Negative Dependence: Theory and Applications in Machine Learning, 2019 | 1 | 2019 |
| Near-Optimal Learning in Sequential Games T Yu Massachusetts Institute of Technology, 2023 | | 2023 |
| Achieving diversity and relevancy in zero-shot recommender systems for human evaluations T Yu, Y Ma, A Deoras | | 2022 |
| A General Framework for Analyzing Stochastic Dynamics in Learning Algorithms CN Chou, JS Sandhu, MB Wang, T Yu arXiv preprint arXiv:2006.06171, 2020 | | 2020 |
Chi JinAssistant Professor, Princeton Universityprinceton.edu의 이메일 확인됨