Jason D. Lee

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	전체	2017년 이후
서지정보	10450	9728
h-index	50	48
i10-index	79	79

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of Washingtoncs.washington.edu의 이메일 확인됨
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeleycs.berkeley.edu의 이메일 확인됨
Yuekai SunAssistant Professor of Statistics, University of Michiganumich.edu의 이메일 확인됨
Tengyu MAStanford Universitystanford.edu의 이메일 확인됨
Nathan SrebroProfessor, TTIC and University of Chicagottic.edu의 이메일 확인됨
Sham M KakadeHarvard Universityseas.harvard.edu의 이메일 확인됨
Suriya GunasekarSenior Researcher, MSR Redmondttic.edu의 이메일 확인됨
Jonathan TaylorProfessor of Statistics, Stanford Universitystanford.edu의 이메일 확인됨
Daniel SoudryAssociate Professortechnion.ac.il의 이메일 확인됨
Qiang LiuAssistant Professor of Computer Science, UT Austincs.utexas.edu의 이메일 확인됨
Qi LeiAssociate Research Scholar at ECE Department, Princeton Universityprinceton.edu의 이메일 확인됨
Meisam RazaviyaynUniversity of Southern Californiausc.edu의 이메일 확인됨
Benjamin RechtAssociate Professor of EECS, University of California, Berkeleyeecs.berkeley.edu의 이메일 확인됨
Rong GeDuke Universitycs.duke.edu의 이메일 확인됨
Gaurav MahajanUniversity of California San Diegoeng.ucsd.edu의 이메일 확인됨
Trevor HastieProfessor of Statistics, Stanford Universitystanford.edu의 이메일 확인됨
MAX SIMCHOWITZMITmit.edu의 이메일 확인됨
Liwei WangProfessor, Peking Universitycis.pku.edu.cn의 이메일 확인됨
Maziar SanjabiMeta AIfb.com의 이메일 확인됨
Wei HuUniversity of California, Berkeleyberkeley.edu의 이메일 확인됨

팔로우

Jason D. Lee

Associate Professor of Electrical Engineering and Computer Science , Princeton University

princeton.edu의 이메일 확인됨 - 홈페이지

Machine Learning Artificial Intelligence Statistics Optimization


제목 서지정보순 정렬 연도순 정렬 제목순 정렬	인용 인용	연도
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	802	2018
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	687*	2016
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	681	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	605	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	423	2014
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	353	2018
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	321	2016
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	265	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	263	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	260	2018
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2018	249	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	248	2019
Optimality and approximation with policy gradient methods in markov decision processes A Agarwal, SM Kakade, JD Lee, G Mahajan Conference on Learning Theory, 64-66, 2020	231	2020
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	231	2017
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	211*	2019
On the power of over-parametrization in neural networks with quadratic activation S Du, J Lee International conference on machine learning, 1329-1338, 2018	200	2018
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	197	2017
Learning the structure of mixed graphical models JD Lee, TJ Hastie Journal of Computational and Graphical Statistics 24 (1), 230-253, 2015	197*	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	196	2018
Practical large-scale optimization for max-norm regularization JD Lee, B Recht, N Srebro, J Tropp, RR Salakhutdinov Advances in neural information processing systems 23, 2010	174	2010

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