Jason D. Lee

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerifierad e-postadress på cs.washington.edu
Yuekai SunUniversity of MichiganVerifierad e-postadress på umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerifierad e-postadress på cs.berkeley.edu
Sham M KakadeHarvard UniversityVerifierad e-postadress på seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityVerifierad e-postadress på nyu.edu
Tengyu MAStanford UniversityVerifierad e-postadress på stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerifierad e-postadress på ttic.edu
Wenhao ZhanGraduate Student, Princeton UniversityVerifierad e-postadress på princeton.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityVerifierad e-postadress på stanford.edu
Wen SunAssistant Professor, Cornell UniversityVerifierad e-postadress på cornell.edu
Suriya GunasekarMSR RedmondVerifierad e-postadress på ttic.edu
Daniel SoudryAssociate ProfessorVerifierad e-postadress på technion.ac.il
Qiang LiuAssociate Professor of Computer Science, UT AustinVerifierad e-postadress på cs.utexas.edu
Alex DamianPh.D. Student, Princeton UniversityVerifierad e-postadress på princeton.edu
Haochuan LiMIT EECSVerifierad e-postadress på mit.edu
Tianle CaiPhD Student, Princeton UniversityVerifierad e-postadress på princeton.edu
Baihe HuangUniversity of California, BerkeleyVerifierad e-postadress på berkeley.edu
Gaurav MahajanPostdoctoral Associate, Yale UniversityVerifierad e-postadress på yale.edu
Eshaan NichaniPrinceton UniversityVerifierad e-postadress på princeton.edu
Liwei WangProfessor, Peking UniversityVerifierad e-postadress på cis.pku.edu.cn

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Jason D. Lee

Andra namnJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Verifierad e-postadress på princeton.edu - Startsida

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	1442	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	1010	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	928*	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	867*	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	674	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	566	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	511	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	500	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	492	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	485*	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	477	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	414	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	408	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
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	314	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	313	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	296	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	293	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	289	2020

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