| 16-qubit IBM universal quantum computer can be fully entangled Y Wang, Y Li, Z Yin, B Zeng npj Quantum Information 4 (1), 1-6, 2018 | 164 | 2018 |
| V-Learning—A Simple, Efficient, Decentralized Algorithm for Multiagent Reinforcement Learning C Jin, Q Liu, Y Wang, T Yu Mathematics of Operations Research, 2023 | 129* | 2023 |
| Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP K Dong, Y Wang, X Chen, L Wang International Conference on Learning Representations 2020, 2019 | 122 | 2019 |
| On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach Y Wang, G Zhang, J Ba International Conference on Learning Representations 2020, 2019 | 120 | 2019 |
| Distributed bandit learning: Near-optimal regret with efficient communication Y Wang, J Hu, X Chen, L Wang arXiv preprint arXiv:1904.06309, 2019 | 101 | 2019 |
| Online learning in unknown markov games Y Tian, Y Wang, T Yu, S Sra International conference on machine learning, 10279-10288, 2021 | 79* | 2021 |
| Improved Algorithms for Convex-Concave Minimax Optimization Y Wang, J Li Neural Information Processing Systems 2020, 2020 | 75 | 2020 |
| Near-optimal local convergence of alternating gradient descent-ascent for minimax optimization G Zhang, Y Wang, L Lessard, RB Grosse International Conference on Artificial Intelligence and Statistics, 7659-7679, 2022 | 67 | 2022 |
| An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap Y Wang, R Wang, S Kakade Advances in Neural Information Processing Systems 34, 2021 | 53 | 2021 |
| Is RLHF More Difficult than Standard RL? A Theoretical Perspective Y Wang, Q Liu, C Jin Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 51* | 2023 |
| Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation Y Wang, Q Liu, Y Bai, C Jin Conference on Learning Theory 2023, 2023 | 31 | 2023 |
| On the suboptimality of negative momentum for minimax optimization G Zhang, Y Wang International Conference on Artificial Intelligence and Statistics, 2020 | 24 | 2020 |
| Learning markov games with adversarial opponents: Efficient algorithms and fundamental limits Q Liu, Y Wang, C Jin Proceedings of the 39th International Conference on Machine Learning, PMLR …, 2022 | 23 | 2022 |
| Directional Smoothness and Gradient Methods: Convergence and Adaptivity A Mishkin, A Khaled, Y Wang, A Defazio, RM Gower arXiv preprint arXiv:2403.04081, 2024 | 5 | 2024 |
| Refined analysis of fpl for adversarial markov decision processes Y Wang, K Dong arXiv preprint arXiv:2008.09251, 2020 | 2 | 2020 |
| Learning Rationalizable Equilibria in Multiplayer Games Y Wang, D Kong, Y Bai, C Jin arXiv preprint arXiv:2210.11402, 2022 | 1 | 2022 |
Chi JinAssistant Professor, Princeton UniversityAdresse e-mail validée de princeton.edu
