Jason D. Lee 个人学术档案 - 学术资源搜索

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	总计	2019 年至今
引用	17079	14888
h 指数	62	57
i10 指数	112	110

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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
Yuekai SunUniversity of Michigan在 umich.edu 的电子邮件经过验证
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.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
Tengyu MAStanford University在 stanford.edu 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
Nathan SrebroProfessor, TTIC and University of Chicago在 ttic.edu 的电子邮件经过验证
Jonathan TaylorProfessor of Statistics, Stanford University在 stanford.edu 的电子邮件经过验证
Suriya GunasekarMSR Redmond在 ttic.edu 的电子邮件经过验证
Daniel SoudryAssociate Professor在 technion.ac.il 的电子邮件经过验证
Wen SunAssistant Professor, Cornell University在 cornell.edu 的电子邮件经过验证
Qiang LiuAssistant Professor of Computer Science, UT Austin在 cs.utexas.edu 的电子邮件经过验证
Wenhao ZhanGraduate Student, Princeton University在 princeton.edu 的电子邮件经过验证
Meisam RazaviyaynUniversity of Southern California在 usc.edu 的电子邮件经过验证
Haochuan LiMIT EECS在 mit.edu 的电子邮件经过验证
Gaurav MahajanPostdoctoral Associate, Yale University在 yale.edu 的电子邮件经过验证
Rong GeDuke University在 cs.duke.edu 的电子邮件经过验证
Liwei WangProfessor, Peking University在 cis.pku.edu.cn 的电子邮件经过验证
Benjamin RechtProfessor of EECS, University of California, Berkeley在 eecs.berkeley.edu 的电子邮件经过验证
Alex DamianPh.D. Student, Princeton University在 princeton.edu 的电子邮件经过验证

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Jason D. Lee

其他姓名Jason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

在 princeton.edu 的电子邮件经过验证 - 首页

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


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
Gradient descent finds global minima of deep neural networks SS Du, JD Lee, H Li, L Wang, X Zhai arXiv preprint arXiv:1811.03804, 2018	1284	2018
Exact post-selection inference, with application to the lasso JD Lee, DL Sun, Y Sun, JE Taylor The Annals of Statistics 44 (3), 907-927, 2016	939	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	863*	2016
On the theory of policy gradient methods: Optimality, approximation, and distribution shift A Agarwal, SM Kakade, JD Lee, G Mahajan Journal of Machine Learning Research 22 (98), 1-76, 2021	737*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	700	2016
Matrix completion and low-rank SVD via fast alternating least squares T Hastie, R Mazumder, J Lee, R Zadeh Journal of Machine Learning Research, 2014	606	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	510	2016
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks M Soltanolkotabi, A Javanmard, JD Lee IEEE Transactions on Information Theory 65 (2), 742-769, 2018	460	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	449*	2014
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	442	2018
Implicit bias of gradient descent on linear convolutional networks S Gunasekar, JD Lee, D Soudry, N Srebro Advances in neural information processing systems 31, 2018	436	2018
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro International Conference on Machine Learning, 1832-1841, 2018	433	2018
Solving a class of non-convex min-max games using iterative first order methods M Nouiehed, M Sanjabi, T Huang, JD Lee, M Razaviyayn Advances in Neural Information Processing Systems 32, 2019	358	2019
Kernel and rich regimes in overparametrized models B Woodworth, S Gunasekar, JD Lee, E Moroshko, P Savarese, I Golan, ... Conference on Learning Theory, 3635-3673, 2020	350	2020
First-order methods almost always avoid strict saddle points JD Lee, I Panageas, G Piliouras, M Simchowitz, MI Jordan, B Recht Mathematical programming 176 (1-2), 311-337, 2019	350	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	298	2017
Gradient descent can take exponential time to escape saddle points SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos Advances in neural information processing systems 30, 2017	286	2017
On the power of over-parametrization in neural networks with quadratic activation S Du, J Lee International conference on machine learning, 1329-1338, 2018	278	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	257	2020
Stochastic subgradient method converges on tame functions D Davis, D Drusvyatskiy, S Kakade, JD Lee Foundations of computational mathematics 20 (1), 119-154, 2020	256	2020

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