| The landscape of empirical risk for nonconvex losses S Mei, Y Bai, A Montanari The Annals of Statistics 46 (6A), 2747-2774, 2018 | 291 | 2018 |
| Provable self-play algorithms for competitive reinforcement learning Y Bai, C Jin International conference on machine learning, 551-560, 2020 | 120 | 2020 |
| Proxquant: Quantized neural networks via proximal operators Y Bai, YX Wang, E Liberty International Conference on Learning Representations (ICLR) 2019, 2018 | 93 | 2018 |
| 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 | 92 | 2021 |
| Near-Optimal Reinforcement Learning with Self-Play Y Bai, C Jin, T Yu Advances in Neural Information Processing Systems, 2020, 2020 | 87 | 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 | 73 | 2021 |
| Approximability of discriminators implies diversity in GANs Y Bai, T Ma, A Risteski International Conference on Learning Representations (ICLR) 2019, 2018 | 72 | 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 | 67 | 2019 |
| 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 | 61* | 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 | 57 | 2019 |
| When can we learn general-sum Markov games with a large number of players sample-efficiently? Z Song, S Mei, Y Bai arXiv preprint arXiv:2110.04184, 2021 | 50 | 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 | 49 | 2021 |
| 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 | 48 | 2021 |
| Subgradient descent learns orthogonal dictionaries Y Bai, Q Jiang, J Sun International Conference on Learning Representations (ICLR) 2019, 2018 | 48 | 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 | 38 | 2021 |
| 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 | 26 | 2020 |
| Taylorized training: Towards better approximation of neural network training at finite width Y Bai, B Krause, H Wang, C Xiong, R Socher arXiv preprint arXiv:2002.04010, 2020 | 18 | 2020 |
| Exact gap between generalization error and uniform convergence in random feature models Z Yang, Y Bai, S Mei International Conference on Machine Learning, 11704-11715, 2021 | 16 | 2021 |
| 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 | 15 | 2021 |
| Tapas: Two-pass approximate adaptive sampling for softmax Y Bai, S Goldman, L Zhang arXiv preprint arXiv:1707.03073, 2017 | 14 | 2017 |
Song MeiAssistant Professor at UC BerkeleyVerified email at berkeley.edu
