Jason D. Lee

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerified email at cs.washington.edu
Yuekai SunUniversity of MichiganVerified email at umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityVerified email at nyu.edu
Tengyu MAStanford UniversityVerified email at stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityVerified email at stanford.edu
Suriya GunasekarMSR RedmondVerified email at ttic.edu
Wenhao ZhanGraduate Student, Princeton UniversityVerified email at princeton.edu
Daniel SoudryAssociate ProfessorVerified email at technion.ac.il
Wen SunAssistant Professor, Cornell UniversityVerified email at cornell.edu
Qiang LiuAssociate Professor of Computer Science, UT AustinVerified email at cs.utexas.edu
Meisam RazaviyaynUniversity of Southern CaliforniaVerified email at usc.edu
Haochuan LiMIT EECSVerified email at mit.edu
Gaurav MahajanPostdoctoral Associate, Yale UniversityVerified email at yale.edu
Liwei WangProfessor, Peking UniversityVerified email at cis.pku.edu.cn
Alex DamianPh.D. Student, Princeton UniversityVerified email at princeton.edu
Rong GeDuke UniversityVerified email at cs.duke.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu

Jason D. Lee

Other namesJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Verified email at princeton.edu - Homepage

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


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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	1346	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	974	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	895*	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	802*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	720	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	625	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	534	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	482	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	471	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	470*	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	456	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	445	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	383	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	368	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	366	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	302	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	287	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	272	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	269	2020

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