Jason D. Lee

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Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonEmail verificata su cs.washington.edu
Yuekai SunUniversity of MichiganEmail verificata su umich.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyEmail verificata su cs.berkeley.edu
Sham M KakadeHarvard UniversityEmail verificata su seas.harvard.edu
Qi LeiAssistant Professor of Mathematics and Data Science, New York UniversityEmail verificata su nyu.edu
Tengyu MAStanford UniversityEmail verificata su stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoEmail verificata su ttic.edu
Jonathan TaylorProfessor of Statistics, Stanford UniversityEmail verificata su stanford.edu
Wenhao ZhanGraduate Student, Princeton UniversityEmail verificata su princeton.edu
Suriya GunasekarMSR RedmondEmail verificata su ttic.edu
Wen SunAssistant Professor, Cornell UniversityEmail verificata su cornell.edu
Daniel SoudryAssociate ProfessorEmail verificata su technion.ac.il
Qiang LiuAssociate Professor of Computer Science, UT AustinEmail verificata su cs.utexas.edu
Haochuan LiMIT EECSEmail verificata su mit.edu
Alex DamianPh.D. Student, Princeton UniversityEmail verificata su princeton.edu
Gaurav MahajanPostdoctoral Associate, Yale UniversityEmail verificata su yale.edu
Tianle CaiPhD Student, Princeton UniversityEmail verificata su princeton.edu
Liwei WangProfessor, Peking UniversityEmail verificata su cis.pku.edu.cn
Rong GeDuke UniversityEmail verificata su cs.duke.edu
Benjamin RechtProfessor of EECS, University of California, BerkeleyEmail verificata su eecs.berkeley.edu

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Jason D. Lee

Altri nomiJason Lee

Associate Professor of Electrical Engineering and Computer Science, Princeton University

Email verificata su princeton.edu - Home page

Machine Learning Theory Machine Learning Artificial Intelligence Statistics Optimization


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	1387	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	990	2016
Gradient descent only converges to minimizers JD Lee, M Simchowitz, MI Jordan, B Recht Conference on learning theory, 1246-1257, 2016	905*	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	841*	2021
Matrix completion has no spurious local minimum R Ge, JD Lee, T Ma Advances in neural information processing systems 29, 2016	729	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	641	2014
A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016	545	2016
Communication-efficient distributed statistical inference MI Jordan, JD Lee, Y Yang Journal of the American Statistical Association, 2019	490	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	476	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	475	2018
Proximal Newton-type methods for minimizing composite functions JD Lee, Y Sun, MA Saunders SIAM Journal on Optimization 24 (3), 1420-1443, 2014	475*	2014
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	461	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	393	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	390	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	377	2019
Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma arXiv preprint arXiv:1711.00501, 2017	306	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	305	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	290	2018
Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei arXiv preprint arXiv:2002.09434, 2020	283	2020
Stochastic subgradient method converges on tame functions D Davis, D Drusvyatskiy, S Kakade, JD Lee Foundations of computational mathematics 20 (1), 119-154, 2020	278	2020

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