Wei Hu

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonPreverjeni e-poštni naslov na cs.washington.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoPreverjeni e-poštni naslov na ttic.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityPreverjeni e-poštni naslov na cs.princeton.edu
Ruosong WangAssistant Professor, Peking UniversityPreverjeni e-poštni naslov na pku.edu.cn
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPreverjeni e-poštni naslov na princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUPreverjeni e-poštni naslov na cs.cmu.edu
Lechao XiaoGoogle DeepMindPreverjeni e-poštni naslov na google.com
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityPreverjeni e-poštni naslov na nyu.edu
Yuping LuoComputer Science Department, Princeton UniversityPreverjeni e-poštni naslov na cs.princeton.edu
Jeffrey PenningtonGoogle BrainPreverjeni e-poštni naslov na google.com
Noah GolowichMITPreverjeni e-poštni naslov na mit.edu
Sham M KakadeHarvard UniversityPreverjeni e-poštni naslov na seas.harvard.edu
Qing QuAssistant Professor, Dept. of EECS, University of MichiganPreverjeni e-poštni naslov na umich.edu
Elad HazanProfessor at Princeton University and Director Google AI PrincetonPreverjeni e-poštni naslov na princeton.edu
Yuanzhi LiAssistant Professor at CMUPreverjeni e-poštni naslov na andrew.cmu.edu
Pravesh K. KothariPrinceton UniversityPreverjeni e-poštni naslov na cs.cmu.edu
Ben AdlamAnthropicPreverjeni e-poštni naslov na anthropic.com
Jacob SteinhardtStanford UniversityPreverjeni e-poštni naslov na cs.stanford.edu
Spencer FreiUC DavisPreverjeni e-poštni naslov na ucdavis.edu
Gal VardiWeizmann Institute of SciencePreverjeni e-poštni naslov na weizmann.ac.il

Spremljaj

Wei Hu

Assistant Professor of Computer Science and Engineering, University of Michigan

Preverjeni e-poštni naslov na umich.edu - Domača stran

Theoretical Machine Learning


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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	1049	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	983	2019
Implicit regularization in deep matrix factorization S Arora, N Cohen, W Hu, Y Luo Advances in Neural Information Processing Systems 32, 2019	556	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	279	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	268	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	226	2018
An analysis of the t-sne algorithm for data visualization S Arora, W Hu, PK Kothari Conference on learning theory, 1455-1462, 2018	191	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	150	2016
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	137	2019
Provable benefit of orthogonal initialization in optimizing deep linear networks W Hu, L Xiao, J Pennington arXiv preprint arXiv:2001.05992, 2020	133	2020
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	131	2019
Width provably matters in optimization for deep linear neural networks S Du, W Hu International Conference on Machine Learning, 1655-1664, 2019	97	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	93	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	87	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	75	2020
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	65	2017
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	63	2022
Impact of representation learning in linear bandits J Yang, W Hu, JD Lee, SS Du arXiv preprint arXiv:2010.06531, 2020	62*	2020
New characterizations in turnstile streams with applications Y Ai, W Hu, Y Li, DP Woodruff 31st Conference on Computational Complexity (CCC 2016), 2016	46	2016
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	45	2022

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