Wei Hu

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at princeton.edu
Ruosong WangAssistant Professor, Peking UniversityVerified email at pku.edu.cn
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Lechao XiaoGoogle DeepMindVerified email at google.com
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityVerified email at nyu.edu
Yuping LuoComputer Science Department, Princeton UniversityVerified email at cs.princeton.edu
Jeffrey PenningtonGoogle BrainVerified email at google.com
Spencer FreiGoogle DeepMindVerified email at google.com
Gal VardiWeizmann Institute of ScienceVerified email at weizmann.ac.il
Noah GolowichMITVerified email at mit.edu
Qing QuAssistant Professor, Dept. of EECS, University of MichiganVerified email at umich.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Pravesh K. KothariPrinceton UniversityVerified email at cs.cmu.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerified email at princeton.edu
Yuanzhi LiAssistant Professor at CMUVerified email at andrew.cmu.edu
Yongyi YangUniversity of MichiganVerified email at umich.edu
Jacob SteinhardtStanford UniversityVerified email at cs.stanford.edu

Wei Hu

Assistant Professor of Computer Science and Engineering, University of Michigan

Verified email at umich.edu - Homepage

Theoretical Machine Learning


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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks S Arora, S Du, W Hu, Z Li, R Wang International Conference on Machine Learning, 322-332, 2019	1116	2019
On exact computation with an infinitely wide neural net S Arora, SS Du, W Hu, Z Li, RR Salakhutdinov, R Wang Advances in neural information processing systems 32, 2019	1055	2019
Implicit regularization in deep matrix factorization S Arora, N Cohen, W Hu, Y Luo Advances in Neural Information Processing Systems 32, 2019	600	2019
A convergence analysis of gradient descent for deep linear neural networks S Arora, N Cohen, N Golowich, W Hu arXiv preprint arXiv:1810.02281, 2018	303	2018
Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei arXiv preprint arXiv:2002.09434, 2020	287	2020
Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced SS Du, W Hu, JD Lee Advances in neural information processing systems 31, 2018	245	2018
An analysis of the t-sne algorithm for data visualization S Arora, W Hu, PK Kothari Conference on learning theory, 1455-1462, 2018	225	2018
Combinatorial multi-armed bandit with general reward functions W Chen, W Hu, F Li, J Li, Y Liu, P Lu Advances in Neural Information Processing Systems 29, 2016	160	2016
Provable benefit of orthogonal initialization in optimizing deep linear networks W Hu, L Xiao, J Pennington arXiv preprint arXiv:2001.05992, 2020	151	2020
Linear convergence of the primal-dual gradient method for convex-concave saddle point problems without strong convexity SS Du, W Hu The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	142	2019
Enhanced convolutional neural tangent kernels Z Li, R Wang, D Yu, SS Du, W Hu, R Salakhutdinov, S Arora arXiv preprint arXiv:1911.00809, 2019	140	2019
Width provably matters in optimization for deep linear neural networks S Du, W Hu International Conference on Machine Learning, 1655-1664, 2019	103	2019
Explaining landscape connectivity of low-cost solutions for multilayer nets R Kuditipudi, X Wang, H Lee, Y Zhang, Z Li, W Hu, R Ge, S Arora Advances in neural information processing systems 32, 2019	97	2019
Simple and effective regularization methods for training on noisily labeled data with generalization guarantee W Hu, Z Li, D Yu arXiv preprint arXiv:1905.11368, 2019	91	2019
The surprising simplicity of the early-time learning dynamics of neural networks W Hu, L Xiao, B Adlam, J Pennington Advances in Neural Information Processing Systems 33, 17116-17128, 2020	80	2020
More than a toy: Random matrix models predict how real-world neural representations generalize A Wei, W Hu, J Steinhardt International Conference on Machine Learning, 23549-23588, 2022	77	2022
Linear convergence of a frank-wolfe type algorithm over trace-norm balls Z Allen-Zhu, E Hazan, W Hu, Y Li Advances in neural information processing systems 30, 2017	67	2017
Impact of representation learning in linear bandits J Yang, W Hu, JD Lee, SS Du arXiv preprint arXiv:2010.06531, 2020	64*	2020
Implicit bias in leaky relu networks trained on high-dimensional data S Frei, G Vardi, PL Bartlett, N Srebro, W Hu arXiv preprint arXiv:2210.07082, 2022	53	2022
Near-optimal linear regression under distribution shift Q Lei, W Hu, J Lee International Conference on Machine Learning, 6164-6174, 2021	47	2021

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