| The landscape of empirical risk for nonconvex losses S Mei, Y Bai, A Montanari The Annals of Statistics 46 (6A), 2747-2774, 2018 | 381 | 2018 |
| Provable self-play algorithms for competitive reinforcement learning Y Bai, C Jin International conference on machine learning, 551-560, 2020 | 189 | 2020 |
| Policy finetuning: Bridging sample-efficient offline and online reinforcement learning T Xie, N Jiang, H Wang, C Xiong, Y Bai Advances in neural information processing systems 34, 27395-27407, 2021 | 180 | 2021 |
| Transformers as statisticians: Provable in-context learning with in-context algorithm selection Y Bai, F Chen, H Wang, C Xiong, S Mei Advances in neural information processing systems 36, 2024 | 176 | 2024 |
| Near-Optimal Reinforcement Learning with Self-Play Y Bai, C Jin, T Yu Advances in Neural Information Processing Systems, 2020, 2020 | 163 | 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 |
| Beyond linearization: On quadratic and higher-order approximation of wide neural networks Y Bai, JD Lee International Conference on Learning Representations (ICLR) 2020, 2019 | 142 | 2019 |
| Proxquant: Quantized neural networks via proximal operators Y Bai, YX Wang, E Liberty International Conference on Learning Representations (ICLR) 2019, 2018 | 127 | 2018 |
| Provably Efficient Q-Learning with Low Switching Cost Y Bai, T Xie, N Jiang, YX Wang Advances in Neural Information Processing Systems, 2019, 2019 | 118 | 2019 |
| When can we learn general-sum Markov games with a large number of players sample-efficiently? Z Song, S Mei, Y Bai International Conference on Learning Representations (ICLR) 2022, 2021 | 114 | 2021 |
| Near-optimal provable uniform convergence in offline policy evaluation for reinforcement learning M Yin, Y Bai, YX Wang International Conference on Artificial Intelligence and Statistics, 1567-1575, 2021 | 98* | 2021 |
| How important is the train-validation split in meta-learning? Y Bai, M Chen, P Zhou, T Zhao, J Lee, S Kakade, H Wang, C Xiong International Conference on Machine Learning, 543-553, 2021 | 90 | 2021 |
| Approximability of discriminators implies diversity in GANs Y Bai, T Ma, A Risteski International Conference on Learning Representations (ICLR) 2019, 2018 | 88 | 2018 |
| Sample-efficient learning of stackelberg equilibria in general-sum games Y Bai, C Jin, H Wang, C Xiong Advances in Neural Information Processing Systems 34, 25799-25811, 2021 | 78 | 2021 |
| The role of coverage in online reinforcement learning T Xie, DJ Foster, Y Bai, N Jiang, SM Kakade arXiv preprint arXiv:2210.04157, 2022 | 76 | 2022 |
| Near-optimal offline reinforcement learning via double variance reduction M Yin, Y Bai, YX Wang Advances in neural information processing systems 34, 7677-7688, 2021 | 74 | 2021 |
| Negative preference optimization: From catastrophic collapse to effective unlearning R Zhang, L Lin, Y Bai, S Mei arXiv preprint arXiv:2404.05868, 2024 | 65 | 2024 |
| Subgradient descent learns orthogonal dictionaries Y Bai, Q Jiang, J Sun International Conference on Learning Representations (ICLR) 2019, 2018 | 59 | 2018 |
| Towards understanding hierarchical learning: Benefits of neural representations M Chen, Y Bai, JD Lee, T Zhao, H Wang, C Xiong, R Socher Advances in Neural Information Processing Systems, 2020, 2020 | 55 | 2020 |
| Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification Y Bai, S Mei, H Wang, C Xiong International Conference on Machine Learning, 566-576, 2021 | 53 | 2021 |
Song MeiAssistant Professor at UC Berkeley在 berkeley.edu 的电子邮件经过验证
