Wei Hu 个人学术档案 - 学术资源搜索

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of Washington在 cs.washington.edu 的电子邮件经过验证
Zhiyuan LiAssistant Professor, Toyota Technological Institute at Chicago在 ttic.edu 的电子邮件经过验证
Sanjeev AroraProfessor of Computer Science, Princeton University在 cs.princeton.edu 的电子邮件经过验证
Ruosong WangPhD Student, Carnegie Mellon University在 andrew.cmu.edu 的电子邮件经过验证
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton University在 princeton.edu 的电子邮件经过验证
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMU在 cs.cmu.edu 的电子邮件经过验证
Nadav CohenAsst. Professor of Computer Science, Tel Aviv University | CTO & Co-Founder, Imubit在 tauex.tau.ac.il 的电子邮件经过验证
Lechao XiaoGoogle Brain在 google.com 的电子邮件经过验证
Qi LeiAssistant Professor of Mathematics and Data Science, New York University在 nyu.edu 的电子邮件经过验证
Yuping LuoComputer Science Department, Princeton University在 cs.princeton.edu 的电子邮件经过验证
Jeffrey PenningtonGoogle Brain在 google.com 的电子邮件经过验证
Noah GolowichMIT在 mit.edu 的电子邮件经过验证
Sham M KakadeHarvard University在 seas.harvard.edu 的电子邮件经过验证
Elad HazanProfessor at Princeton University and Director Google AI Princeton在 princeton.edu 的电子邮件经过验证
Yuanzhi LiAssistant Professor at CMU在 andrew.cmu.edu 的电子邮件经过验证
Pravesh K. KothariPrinceton University在 cs.cmu.edu 的电子邮件经过验证
Ben AdlamGoogle DeepMind在 google.com 的电子邮件经过验证
Qing QuAssistant Professor, Dept. of EECS, University of Michigan在 umich.edu 的电子邮件经过验证
Wei Chen （陈卫）Microsoft Research在 microsoft.com 的电子邮件经过验证
Pinyan LuITCS, Shanghai University of Finance and Economics在 mail.shufe.edu.cn 的电子邮件经过验证

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Wei Hu

Assistant Professor of Computer Science and Engineering, University of Michigan

在 umich.edu 的电子邮件经过验证 - 首页

Theoretical Machine Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	1002	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	948	2019
Implicit regularization in deep matrix factorization S Arora, N Cohen, W Hu, Y Luo Advances in Neural Information Processing Systems 32, 2019	521	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	261	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	256	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	214	2018
An analysis of the t-sne algorithm for data visualization S Arora, W Hu, PK Kothari Conference on learning theory, 1455-1462, 2018	173	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	143	2016
Provable benefit of orthogonal initialization in optimizing deep linear networks W Hu, L Xiao, J Pennington arXiv preprint arXiv:2001.05992, 2020	129	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	127	2019
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	126	2019
Width provably matters in optimization for deep linear neural networks S Du, W Hu International Conference on Machine Learning, 1655-1664, 2019	93	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	87	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	82	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	71	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	63	2017
Impact of representation learning in linear bandits J Yang, W Hu, JD Lee, SS Du International Conference on Learning Representations, 2021	58*	2021
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	55	2022
New characterizations in turnstile streams with applications Y Ai, W Hu, Y Li, DP Woodruff 31st Conference on Computational Complexity (CCC 2016), 2016	45	2016
Near-optimal linear regression under distribution shift Q Lei, W Hu, J Lee International Conference on Machine Learning, 6164-6174, 2021	40	2021

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