Jason D. Lee

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	Toutes	Depuis 2015
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Coauteurs

Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Yuekai SunAssistant Professor of Statistics, University of MichiganAdresse e-mail validée de umich.edu
Nathan SrebroProfessor, TTIC and University of ChicagoAdresse e-mail validée de ttic.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
Daniel SoudryAssistant ProfessorAdresse e-mail validée de technion.ac.il
Suriya GunasekarSenior Researcher, MSR RedmondAdresse e-mail validée de ttic.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyAdresse e-mail validée de eecs.berkeley.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
Meisam RazaviyaynUniversity of Southern CaliforniaAdresse e-mail validée de usc.edu
Trevor HastieProfessor of Statistics, 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
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Michael SaundersProfessor of Operations Research, Stanford UniversityAdresse e-mail validée de stanford.edu
Sham M KakadeUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Dennis SunData Scientist at Google; Professor at Cal PolyAdresse e-mail validée de calpoly.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	513*	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	493	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in Neural Information Processing Systems, 2973-2981, 2016	449	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	354	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	273	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	215	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	208	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	188	2016
Learning the structure of mixed graphical models JD Lee, TJ Hastie Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015	155*	2015
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	155	2010
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	153	2017
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	149	2018
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	122	2018
On the power of over-parametrization in neural networks with quadratic activation SS Du, JD Lee arXiv preprint arXiv:1803.01206, 2018	122	2018
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	122	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	120	2017
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro arXiv preprint arXiv:1802.08246, 2018	115	2018
Proximal Newton-type methods for convex optimization JD Lee, Y Sun, M Saunders Advances in Neural Information Processing Systems, 827-835, 2012	106	2012
First-order methods almost always avoid saddle points JD Lee, I Panageas, G Piliouras, M Simchowitz, MI Jordan, B Recht arXiv preprint arXiv:1710.07406, 2017	101	2017
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	95	2013

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