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Yu Bai
Yu Bai
Research Scientist, Salesforce Research
Bestätigte E-Mail-Adresse bei salesforce.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
The landscape of empirical risk for nonconvex losses
S Mei, Y Bai, A Montanari
The Annals of Statistics 46 (6A), 2747-2774, 2018
2802018
Provable self-play algorithms for competitive reinforcement learning
Y Bai, C Jin
International conference on machine learning, 551-560, 2020
1012020
Proxquant: Quantized neural networks via proximal operators
Y Bai, YX Wang, E Liberty
International Conference on Learning Representations (ICLR) 2019, 2018
882018
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
732021
Near-Optimal Reinforcement Learning with Self-Play
Y Bai, C Jin, T Yu
Advances in Neural Information Processing Systems, 2020, 2020
722020
Approximability of discriminators implies diversity in GANs
Y Bai, T Ma, A Risteski
International Conference on Learning Representations (ICLR) 2019, 2018
662018
Provably Efficient Q-Learning with Low Switching Cost
Y Bai, T Xie, N Jiang, YX Wang
Advances in Neural Information Processing Systems, 2019, 2019
562019
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
55*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
542019
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
462021
Subgradient descent learns orthogonal dictionaries
Y Bai, Q Jiang, J Sun
International Conference on Learning Representations (ICLR) 2019, 2018
452018
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
402021
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
402021
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
362021
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
252021
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
202020
Tapas: Two-pass approximate adaptive sampling for softmax
Y Bai, S Goldman, L Zhang
arXiv preprint arXiv:1707.03073, 2017
142017
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
132020
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
122021
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
112021
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