Jason D. Lee

Получаване на мой собствен потребителски профил

Позовавания

	Всички	От 2018
Позовавания	14763	13478
h-индекс	55	54
i10-индекс	97	96

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Публичен достъп

Преглед на всички

56 статии

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Съавтори

Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonПотвърден имейл адрес: cs.washington.edu
Yuekai SunUniversity of MichiganПотвърден имейл адрес: umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyПотвърден имейл адрес: cs.berkeley.edu
Nathan SrebroProfessor, TTIC and University of ChicagoПотвърден имейл адрес: ttic.edu
Tengyu MAStanford UniversityПотвърден имейл адрес: stanford.edu
Sham M KakadeHarvard UniversityПотвърден имейл адрес: seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityПотвърден имейл адрес: nyu.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityПотвърден имейл адрес: stanford.edu
Suriya GunasekarMSR RedmondПотвърден имейл адрес: ttic.edu
Daniel SoudryAssociate ProfessorПотвърден имейл адрес: technion.ac.il
Qiang LiuAssistant Professor of Computer Science, UT AustinПотвърден имейл адрес: cs.utexas.edu
Meisam RazaviyaynUniversity of Southern CaliforniaПотвърден имейл адрес: usc.edu
Haochuan LiMIT EECSПотвърден имейл адрес: mit.edu
Rong GeDuke UniversityПотвърден имейл адрес: cs.duke.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyПотвърден имейл адрес: eecs.berkeley.edu
Liwei WangProfessor, Peking UniversityПотвърден имейл адрес: cis.pku.edu.cn
Gaurav MahajanYale UniversityПотвърден имейл адрес: eng.ucsd.edu
Trevor HastieProfessor of Statistics, Stanford UniversityПотвърден имейл адрес: stanford.edu
Baihe HuangUniversity of California, BerkeleyПотвърден имейл адрес: berkeley.edu
MAX SIMCHOWITZMITПотвърден имейл адрес: mit.edu

Следене

Jason D. Lee

Други именаJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Потвърден имейл адрес: princeton.edu - Начална страница

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Заглавие Сортиране по цитати Сортиране по година Сортиране по заглавие	Позовавания Позовавания	Година
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	1137	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	878	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	817*	2016
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	679	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	585*	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	556	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	423*	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	380	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	373	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	319*	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	279	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	263	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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