Jason D. Lee

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	すべて	2019 年以来
引用	17933	15735
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i10 指標	117	115

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of Washington確認したメールアドレス: cs.washington.edu
Yuekai SunUniversity of Michigan確認したメールアドレス: umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley確認したメールアドレス: cs.berkeley.edu
Sham M KakadeHarvard University確認したメールアドレス: seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York University確認したメールアドレス: nyu.edu
Tengyu MAStanford University確認したメールアドレス: stanford.edu
Nathan SrebroProfessor, TTIC and University of Chicago確認したメールアドレス: ttic.edu
Jonathan TaylorProfessor of Statistics, Stanford University確認したメールアドレス: stanford.edu
Suriya GunasekarMSR Redmond確認したメールアドレス: ttic.edu
Daniel SoudryAssociate Professor確認したメールアドレス: technion.ac.il
Wen SunAssistant Professor, Cornell University確認したメールアドレス: cornell.edu
Wenhao ZhanGraduate Student, Princeton University確認したメールアドレス: princeton.edu
Qiang LiuAssociate Professor of Computer Science, UT Austin確認したメールアドレス: cs.utexas.edu
Meisam RazaviyaynUniversity of Southern California確認したメールアドレス: usc.edu
Haochuan LiMIT EECS確認したメールアドレス: mit.edu
Gaurav MahajanPostdoctoral Associate, Yale University確認したメールアドレス: yale.edu
Liwei WangProfessor, Peking University確認したメールアドレス: cis.pku.edu.cn
Rong GeDuke University確認したメールアドレス: cs.duke.edu
Alex DamianPh.D. Student, Princeton University確認したメールアドレス: princeton.edu
Benjamin RechtProfessor of EECS, University of California, Berkeley確認したメールアドレス: eecs.berkeley.edu

フォロー

Jason D. Lee

その他の名前Jason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

確認したメールアドレス: princeton.edu - ホームページ

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	1340	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	971	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	891*	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	796*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	716	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	623	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	533	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	478	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	468	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	466*	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	449	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	380	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	366	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	364	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	301	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	295	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	284	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	270	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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