Jason D. Lee

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonDirección de correo verificada de cs.washington.edu
Yuekai SunUniversity of MichiganDirección de correo verificada de umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityDirección de correo verificada de nyu.edu
Tengyu MAStanford UniversityDirección de correo verificada de stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoDirección de correo verificada de ttic.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityDirección de correo verificada de stanford.edu
Suriya GunasekarMSR RedmondDirección de correo verificada de ttic.edu
Daniel SoudryAssociate ProfessorDirección de correo verificada de technion.ac.il
Wenhao ZhanGraduate Student, Princeton UniversityDirección de correo verificada de princeton.edu
Wen SunAssistant Professor, Cornell UniversityDirección de correo verificada de cornell.edu
Qiang LiuAssociate Professor of Computer Science, UT AustinDirección de correo verificada de cs.utexas.edu
Meisam RazaviyaynUniversity of Southern CaliforniaDirección de correo verificada de usc.edu
Haochuan LiMIT EECSDirección de correo verificada de mit.edu
Gaurav MahajanPostdoctoral Associate, Yale UniversityDirección de correo verificada de yale.edu
Liwei WangProfessor, Peking UniversityDirección de correo verificada de cis.pku.edu.cn
Rong GeDuke UniversityDirección de correo verificada de cs.duke.edu
Alex DamianPh.D. Student, Princeton UniversityDirección de correo verificada de princeton.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyDirección de correo verificada de eecs.berkeley.edu

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Jason D. Lee

Otros nombresJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Dirección de correo verificada de princeton.edu - Página principal

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	1332	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	967	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	887*	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	790*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	712	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	619	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	529	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	472	2019
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	467	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	463*	2014
Characterizing implicit bias in terms of optimization geometry S Gunasekar, J Lee, D Soudry, N Srebro International Conference on Machine Learning, 1832-1841, 2018	448	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	444	2018
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	377	2019
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	364	2020
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	363	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	301	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	296	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	284	2018
Stochastic subgradient method converges on tame functions D Davis, D Drusvyatskiy, S Kakade, JD Lee Foundations of computational mathematics 20 (1), 119-154, 2020	269	2020
Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei arXiv preprint arXiv:2002.09434, 2020	268	2020

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