Jason D. Lee

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

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Jason D. Lee

Andre navneJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Verificeret mail på princeton.edu - Startside

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	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	1438	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	1009	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	926*	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	865*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	735	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	671	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	563	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	510	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	496	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	489	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	472	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	409	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	313	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	295	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	291	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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