Jason D. Lee

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonPreverjeni e-poštni naslov na cs.washington.edu
Yuekai SunUniversity of MichiganPreverjeni e-poštni naslov na umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyPreverjeni e-poštni naslov na cs.berkeley.edu
Sham M KakadeHarvard UniversityPreverjeni e-poštni naslov na seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityPreverjeni e-poštni naslov na nyu.edu
Tengyu MAStanford UniversityPreverjeni e-poštni naslov na stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoPreverjeni e-poštni naslov na ttic.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Wenhao ZhanGraduate Student, Princeton UniversityPreverjeni e-poštni naslov na princeton.edu
Wen SunAssistant Professor, Cornell UniversityPreverjeni e-poštni naslov na cornell.edu
Suriya GunasekarMSR RedmondPreverjeni e-poštni naslov na ttic.edu
Daniel SoudryAssociate ProfessorPreverjeni e-poštni naslov na technion.ac.il
Qiang LiuAssociate Professor of Computer Science, UT AustinPreverjeni e-poštni naslov na cs.utexas.edu
Haochuan LiMIT EECSPreverjeni e-poštni naslov na mit.edu
Alex DamianPh.D. Student, Princeton UniversityPreverjeni e-poštni naslov na princeton.edu
Gaurav MahajanPostdoctoral Associate, Yale UniversityPreverjeni e-poštni naslov na yale.edu
Tianle CaiPhD Student, Princeton UniversityPreverjeni e-poštni naslov na princeton.edu
Liwei WangProfessor, Peking UniversityPreverjeni e-poštni naslov na cis.pku.edu.cn
Rong GeDuke UniversityPreverjeni e-poštni naslov na cs.duke.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyPreverjeni e-poštni naslov na eecs.berkeley.edu

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Jason D. Lee

Druga imenaJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Preverjeni e-poštni naslov na princeton.edu - Domača stran

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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	1395	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	992	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	909*	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	843*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	730	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	644	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	546	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	492	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	479	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	476	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	476*	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	463	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	395	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	392	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	378	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	309	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	307	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	291	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	283	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	280	2020

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