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
Nathan SrebroProfessor, TTIC and University of ChicagoVerified email at ttic.edu
Tengyu MAStanford UniversityVerified email at stanford.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
Jonathan TaylorProfessor of Statistics, Stanford UniversityVerified email at stanford.edu
Suriya GunasekarMSR RedmondVerified email at ttic.edu
Daniel SoudryAssociate ProfessorVerified email at technion.ac.il
Qiang LiuAssistant 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
Rong GeDuke UniversityVerified email at cs.duke.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Liwei WangProfessor, Peking UniversityVerified email at cis.pku.edu.cn
Gaurav MahajanYale UniversityVerified email at eng.ucsd.edu
Trevor HastieProfessor of Statistics, Stanford UniversityVerified email at stanford.edu
Baihe HuangUniversity of California, BerkeleyVerified email at berkeley.edu
MAX SIMCHOWITZMITVerified email at mit.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	1142	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	879	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	819*	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	681	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	588*	2021
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	557	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	463	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	435	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	424*	2014
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	386	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	382	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	374	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	321*	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	318	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	284	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	281	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	264	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	259	2018
Learning the structure of mixed graphical models JD Lee, TJ Hastie Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015	233*	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	230	2018

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