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How Important is the Train-Validation Split in Meta-Learning?

Oct 12, 2020
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham Kakade, Huan Wang, Caiming Xiong


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A Sharp Analysis of Model-based Reinforcement Learning with Self-Play

Oct 04, 2020
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin


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Near-Optimal Reinforcement Learning with Self-Play

Jul 14, 2020
Yu Bai, Chi Jin, Tiancheng Yu


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Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning

Jul 07, 2020
Ming Yin, Yu Bai, Yu-Xiang Wang

* Appendix included 

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Towards Understanding Hierarchical Learning: Benefits of Neural Representations

Jun 24, 2020
Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher


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Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width

Feb 24, 2020
Yu Bai, Ben Krause, Huan Wang, Caiming Xiong, Richard Socher


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Provable Self-Play Algorithms for Competitive Reinforcement Learning

Feb 23, 2020
Yu Bai, Chi Jin


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Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning

Oct 21, 2019
Ke Zhan, Shimiao Jiang, Yu Bai, Yi Li, Xu Liu, Zhuoran Xu

* Proceedings of the British Machine Vision Conference (BMVC), 2019 

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Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks

Oct 03, 2019
Yu Bai, Jason D. Lee


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Provably Efficient Q-Learning with Low Switching Cost

May 30, 2019
Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang


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Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs

Mar 01, 2019
Yu Bai, John Duchi, Song Mei


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Subgradient Descent Learns Orthogonal Dictionaries

Oct 25, 2018
Yu Bai, Qijia Jiang, Ju Sun


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ProxQuant: Quantized Neural Networks via Proximal Operators

Oct 08, 2018
Yu Bai, Yu-Xiang Wang, Edo Liberty


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Approximability of Discriminators Implies Diversity in GANs

Jul 24, 2018
Yu Bai, Tengyu Ma, Andrej Risteski


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CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

Aug 29, 2017
Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, Xiaolong Ma, Yipeng Zhang, Jian Tang, Qinru Qiu, Xue Lin, Bo Yuan

* 14 pages, 15 Figures, conference 

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TAPAS: Two-pass Approximate Adaptive Sampling for Softmax

Jul 14, 2017
Yu Bai, Sally Goldman, Li Zhang


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The Landscape of Empirical Risk for Non-convex Losses

Jan 14, 2017
Song Mei, Yu Bai, Andrea Montanari

* This version presents a general framework, and applies it to several statistical learning problems 

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