Jason D. Lee

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonAdresse e-mail validée de cs.washington.edu
Yuekai SunAssistant Professor of Statistics, University of MichiganAdresse e-mail validée de umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Tengyu MAStanford UniversityAdresse e-mail validée de stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoAdresse e-mail validée de ttic.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityAdresse e-mail validée de stanford.edu
Sham M KakadeHarvard UniversityAdresse e-mail validée de seas.harvard.edu
Suriya GunasekarSenior Researcher, MSR RedmondAdresse e-mail validée de ttic.edu
Daniel SoudryAssociate ProfessorAdresse e-mail validée de technion.ac.il
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityAdresse e-mail validée de nyu.edu
Qiang LiuAssistant Professor of Computer Science, UT AustinAdresse e-mail validée de cs.utexas.edu
Meisam RazaviyaynUniversity of Southern CaliforniaAdresse e-mail validée de usc.edu
Benjamin RechtAssociate Professor of EECS, University of California, BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Haochuan LiMITAdresse e-mail validée de mit.edu
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Gaurav MahajanUniversity of California San DiegoAdresse e-mail validée de eng.ucsd.edu
Trevor HastieProfessor of Statistics, Stanford UniversityAdresse e-mail validée de stanford.edu
Liwei WangProfessor, Peking UniversityAdresse e-mail validée de cis.pku.edu.cn
MAX SIMCHOWITZMITAdresse e-mail validée de mit.edu
Maziar SanjabiMeta AIAdresse e-mail validée de fb.com

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Jason D. Lee

Autres nomsJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

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

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	916	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	788*	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	734*	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	631	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	468	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	384	2018
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	370	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	307	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	299	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	291	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	285	2014
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	270	2019
Optimality and approximation with policy gradient methods in markov decision processes A Agarwal, SM Kakade, JD Lee, G Mahajan Conference on Learning Theory, 64-66, 2020	267	2020
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	255*	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	251	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	224	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	217	2018
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	214	2018
Learning the structure of mixed graphical models JD Lee, TJ Hastie Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015	207*	2015
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	192	2020

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