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 SunUniversity 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
Sham M KakadeHarvard UniversityAdresse e-mail validée de seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityAdresse e-mail validée de nyu.edu
Tengyu MAStanford UniversityAdresse e-mail validée de stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoAdresse e-mail validée de ttic.edu
Wenhao ZhanGraduate Student, Princeton UniversityAdresse e-mail validée de princeton.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityAdresse e-mail validée de stanford.edu
Wen SunAssistant Professor, Cornell UniversityAdresse e-mail validée de cornell.edu
Suriya GunasekarMSR RedmondAdresse e-mail validée de ttic.edu
Daniel SoudryAssociate ProfessorAdresse e-mail validée de technion.ac.il
Qiang LiuAssociate Professor of Computer Science, UT AustinAdresse e-mail validée de cs.utexas.edu
Alex DamianPh.D. Student, Princeton UniversityAdresse e-mail validée de princeton.edu
Haochuan LiMIT EECSAdresse e-mail validée de mit.edu
Baihe HuangUniversity of California, BerkeleyAdresse e-mail validée de berkeley.edu
Tianle CaiPhD Student, Princeton UniversityAdresse e-mail validée de princeton.edu
Gaurav MahajanPostdoctoral Associate, Yale UniversityAdresse e-mail validée de yale.edu
Liwei WangProfessor, Peking UniversityAdresse e-mail validée de cis.pku.edu.cn
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu

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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	1435	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	1008	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	922*	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	864*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	734	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	670	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	560	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	509	2019
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro International Conference on Machine Learning, 1832-1841, 2018	493	2018
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	488	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	484*	2014
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	471	2018
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	411	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	410	2019
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	384	2019
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	313	2017
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	312	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	294	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	289	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	288	2020

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