Yu Bai

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Song MeiAssistant Professor at UC BerkeleyGeverifieerd e-mailadres voor berkeley.edu
Huan WangSalesforce ResearchGeverifieerd e-mailadres voor yale.edu
Caiming XiongSalesforce ResearchGeverifieerd e-mailadres voor salesforce.com
Chi JinAssistant Professor, Princeton UniversityGeverifieerd e-mailadres voor princeton.edu
Yu-Xiang WangAssociate Professor @ UC San DiegoGeverifieerd e-mailadres voor ucsd.edu
Nan JiangAssociate Professor of Computer Science, UIUCGeverifieerd e-mailadres voor illinois.edu
Tiancheng YuTwo SigmaGeverifieerd e-mailadres voor mit.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityGeverifieerd e-mailadres voor princeton.edu
Fan ChenMassachusetts Institute of TechnologyGeverifieerd e-mailadres voor mit.edu
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-MadisonGeverifieerd e-mailadres voor cs.wisc.edu
Minshuo ChenNorthwestern UniversityGeverifieerd e-mailadres voor northwestern.edu
Andrea MontanariJohn D. and Sigrid Banks Professor, Statistics and Mathematics, Stanford UniversityGeverifieerd e-mailadres voor stanford.edu
Qinghua LiuMicrosoft ResearchGeverifieerd e-mailadres voor princeton.edu
Ming YinPrinceton UniversityGeverifieerd e-mailadres voor princeton.edu
Ziang SongStanford UniversityGeverifieerd e-mailadres voor stanford.edu
Sham M KakadeHarvard UniversityGeverifieerd e-mailadres voor seas.harvard.edu
Tuo ZhaoAssociate Professor, Georgia TechGeverifieerd e-mailadres voor gatech.edu
Tianyu GuoPh.D. student, UC BerkeleyGeverifieerd e-mailadres voor berkeley.edu
Licong LinPhD student at UC BerkeleyGeverifieerd e-mailadres voor berkeley.edu
Mengdi WangCenter for Statistics & Machine Learning, ECE, Princeton UniversityGeverifieerd e-mailadres voor princeton.edu

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Yu Bai

OpenAI

Geverifieerd e-mailadres voor openai.com - Homepage

Machine Learning Statistics


Titel Sorteren op citaties Sorteren op jaar Sorteren op titel	Geciteerd door Geciteerd door	Jaar
The landscape of empirical risk for nonconvex losses S Mei, Y Bai, A Montanari The Annals of Statistics 46 (6A), 2747-2774, 2018	385	2018
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	190	2024
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	184	2021
Near-Optimal Reinforcement Learning with Self-Play Y Bai, C Jin, T Yu Advances in Neural Information Processing Systems, 2020, 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
Beyond linearization: On quadratic and higher-order approximation of wide neural networks Y Bai, JD Lee International Conference on Learning Representations (ICLR) 2020, 2019	144	2019
Proxquant: Quantized neural networks via proximal operators Y Bai, YX Wang, E Liberty International Conference on Learning Representations (ICLR) 2019, 2018	128	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	119	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	115	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	76	2021
Negative preference optimization: From catastrophic collapse to effective unlearning R Zhang, L Lin, Y Bai, S Mei arXiv preprint arXiv:2404.05868, 2024	68	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	57	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	55	2021

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