Jason D. Lee - Citations Google Scholar

Citée par

	Toutes	Depuis 2015
Citations	5033	4790
indice h	37	37
indice i10	52	52

1800

900

450

1350

201120122013201420152016201720182019202020 16 56 115 173 285 558 961 1752 1037

Coauteurs

Michael I. JordanProfessor of EECS and Professor of Statistics, University of California, BerkeleyAdresse e-mail validée de cs.berkeley.edu
Yuekai SunAssistant Professor of Statistics, University of MichiganAdresse e-mail validée de umich.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonAdresse e-mail validée de cs.washington.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityAdresse e-mail validée de stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoAdresse e-mail validée de ttic.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Daniel SoudryAssistant ProfessorAdresse e-mail validée de technion.ac.il
Suriya GunasekarSenior Researcher, MSR RedmondAdresse e-mail validée de ttic.edu
Qiang LiuAssistant Professor of Computer Science, UT AustinAdresse e-mail validée de cs.utexas.edu
MAX SIMCHOWITZUC BerkeleyAdresse e-mail validée de berkeley.edu
Trevor HastieProfessor of Statistics, Stanford UniversityAdresse e-mail validée de stanford.edu
Meisam RazaviyaynUniversity of Southern CaliforniaAdresse e-mail validée de usc.edu
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Michael SaundersProfessor of Operations Research, Stanford UniversityAdresse e-mail validée de stanford.edu
Haochuan LiMITAdresse e-mail validée de mit.edu
Liwei WangPeking UniversityAdresse e-mail validée de cis.pku.edu.cn
Maziar SanjabiFacebook AIAdresse e-mail validée de fb.com
Dennis SunData Scientist at Google; Professor at Cal PolyAdresse e-mail validée de calpoly.edu
Sham M KakadeUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu

Jason D. Lee

Assistant Professor of Electrical Engineering, Princeton University

Adresse e-mail validée de princeton.edu - Page d'accueil

Machine Learning Statistics Optimization


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	455*	2016
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	431	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in Neural Information Processing Systems, 2973-2981, 2016	403	2016
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	249	2018
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	244	2014
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	187	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	186	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	158	2016
Practical large-scale optimization for max-norm regularization JD Lee, B Recht, N Srebro, J Tropp, RR Salakhutdinov Advances in neural information processing systems, 1297-1305, 2010	145	2010
Learning the structure of mixed graphical models JD Lee, TJ Hastie Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015	142*	2015
Gradient descent learns one-hidden-layer cnn: Don’t be afraid of spurious local minima S Du, J Lee, Y Tian, A Singh, B Poczos International Conference on Machine Learning, 1339-1348, 2018	128	2018
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	126	2017
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	120*	2019
On the power of over-parametrization in neural networks with quadratic activation SS Du, JD Lee arXiv preprint arXiv:1803.01206, 2018	107	2018
Proximal Newton-type methods for convex optimization JD Lee, Y Sun, M Saunders Advances in Neural Information Processing Systems, 827-835, 2012	103	2012
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	99	2018
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, 1067-1077, 2017	99	2017
Implicit bias of gradient descent on linear convolutional networks S Gunasekar, JD Lee, D Soudry, N Srebro Advances in Neural Information Processing Systems, 9461-9471, 2018	95	2018
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro arXiv preprint arXiv:1802.08246, 2018	93	2018
Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices H Monajemi, S Jafarpour, M Gavish, DL Donoho, ... Proceedings of the National Academy of Sciences 110 (4), 1181-1186, 2013	87	2013

Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.

Articles 1–20

Nombre de citations par an

Citations en double

Citations fusionnées

Ajouter les coauteursCoauteurs

Importer un PDF

Suivre les mises à jour de cet auteur

Citée par

Coauteurs