Jason D. Lee

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	Sve	Od 2018.
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i10-indeks	91	89

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonPotvrđena adresa e-pošte na cs.washington.edu
Yuekai SunUniversity of MichiganPotvrđena adresa e-pošte na umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyPotvrđena adresa e-pošte na cs.berkeley.edu
Nathan SrebroProfessor, TTIC and University of ChicagoPotvrđena adresa e-pošte na ttic.edu
Tengyu MAStanford UniversityPotvrđena adresa e-pošte na stanford.edu
Sham M KakadeHarvard UniversityPotvrđena adresa e-pošte na seas.harvard.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityPotvrđena adresa e-pošte na stanford.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityPotvrđena adresa e-pošte na nyu.edu
Suriya GunasekarSenior Researcher, MSR RedmondPotvrđena adresa e-pošte na ttic.edu
Daniel SoudryAssociate ProfessorPotvrđena adresa e-pošte na technion.ac.il
Qiang LiuAssistant Professor of Computer Science, UT AustinPotvrđena adresa e-pošte na cs.utexas.edu
Meisam RazaviyaynUniversity of Southern CaliforniaPotvrđena adresa e-pošte na usc.edu
Haochuan LiMIT EECSPotvrđena adresa e-pošte na mit.edu
Rong GeDuke UniversityPotvrđena adresa e-pošte na cs.duke.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyPotvrđena adresa e-pošte na eecs.berkeley.edu
Liwei WangProfessor, Peking UniversityPotvrđena adresa e-pošte na cis.pku.edu.cn
Gaurav MahajanYale UniversityPotvrđena adresa e-pošte na eng.ucsd.edu
Trevor HastieProfessor of Statistics, Stanford UniversityPotvrđena adresa e-pošte na stanford.edu
MAX SIMCHOWITZMITPotvrđena adresa e-pošte na mit.edu
Maziar SanjabiMeta AIPotvrđena adresa e-pošte na fb.com

Prati

Jason D. Lee

Ostala imenaJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Potvrđena adresa e-pošte na princeton.edu - Početna stranica

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
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	1100	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	865	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	801*	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	668	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	542	2014
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	536*	2021
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	443	2016
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	423	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	415*	2014
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	367	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	359	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	359	2018
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	313	2019
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	305*	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	275	2017
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	259	2020
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	257	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	251	2018
Learning the structure of mixed graphical models JD Lee, TJ Hastie Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015	227*	2015
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	223	2018

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