Chi Jin - Google Scholar

Citée par

	Toutes	Depuis 2015
Citations	2635	2622
indice h	21	21
indice i10	26	26

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630

20152016201720182019202020 120 266 563 838 804

Coauteurs

Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Praneeth NetrapalliMicrosoft ResearchAdresse e-mail validée de microsoft.com
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Sham M KakadeUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Aaron SidfordStanford UniversityAdresse e-mail validée de stanford.edu
Prateek JainMicrosoft Research IndiaAdresse e-mail validée de microsoft.com
Furong HuangUniversity of MarylandAdresse e-mail validée de cs.umd.edu
Yang YuanMassachusetts Institute of TechnologyAdresse e-mail validée de cs.cornell.edu
Cameron MuscoAssistant Professor, University of Massachusetts AmherstAdresse e-mail validée de mit.edu
Dan GarberAssistant Professor at Technion - Israel Institute of TechnologyAdresse e-mail validée de technion.ac.il
Elad HazanProfessor at Princeton University and Director Google AI PrincetonAdresse e-mail validée de cs.princeton.edu
Zeyuan Allen-Zhu （朱澤園）Microsoft Research RedmondAdresse e-mail validée de csail.mit.edu
Sebastien BubeckSr Principal Researcher, Microsoft ResearchAdresse e-mail validée de microsoft.com
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonAdresse e-mail validée de cs.washington.edu
Jason D. LeeAssistant Professor of Electrical Engineering, Princeton UniversityAdresse e-mail validée de princeton.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.edu
Yuchen ZhangSenior Researcher at MicrosoftAdresse e-mail validée de microsoft.com
Sivaraman BalakrishnanCarnegie Mellon UniversityAdresse e-mail validée de stat.cmu.edu
Martin WainwrightProfessor of EECS and Statistics, UC BerkeleyAdresse e-mail validée de berkeley.edu

Chi Jin

Assistant Professor, Princeton University

Adresse e-mail validée de princeton.edu - Page d'accueil

Machine Learning Optimization


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Escaping from saddle points—online stochastic gradient for tensor decomposition R Ge, F Huang, C Jin, Y Yuan Conference on Learning Theory, 797-842, 2015	700	2015
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan arXiv preprint arXiv:1703.00887, 2017	403	2017
No spurious local minima in nonconvex low rank problems: A unified geometric analysis R Ge, C Jin, Y Zheng arXiv preprint arXiv:1704.00708, 2017	245	2017
Is Q-learning provably efficient? C Jin, Z Allen-Zhu, S Bubeck, MI Jordan Advances in Neural Information Processing Systems, 4863-4873, 2018	157	2018
Accelerated gradient descent escapes saddle points faster than gradient descent C Jin, P Netrapalli, MI Jordan Conference On Learning Theory, 1042-1085, 2018	119	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, 1067-1077, 2017	110	2017
Streaming PCA: Matching matrix Bernstein and near-optimal finite sample guarantees for Oja’s algorithm P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford Conference on learning theory, 1147-1164, 2016	99*	2016
Faster eigenvector computation via shift-and-invert preconditioning D Garber, E Hazan, C Jin, C Musco, P Netrapalli, A Sidford International Conference on Machine Learning, 2626-2634, 2016	89*	2016
Stochastic cubic regularization for fast nonconvex optimization N Tripuraneni, M Stern, C Jin, J Regier, MI Jordan Advances in neural information processing systems, 2899-2908, 2018	81	2018
Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences C Jin, Y Zhang, S Balakrishnan, MJ Wainwright, MI Jordan Advances in neural information processing systems, 4116-4124, 2016	78	2016
Provable efficient online matrix completion via non-convex stochastic gradient descent C Jin, SM Kakade, P Netrapalli Advances in Neural Information Processing Systems, 4520-4528, 2016	69	2016
What is local optimality in nonconvex-nonconcave minimax optimization? C Jin, P Netrapalli, MI Jordan arXiv preprint arXiv:1902.00618, 2019	66*	2019
Efficient algorithms for large-scale generalized eigenvector computation and canonical correlation analysis R Ge, C Jin, P Netrapalli, A Sidford International Conference on Machine Learning, 2741-2750, 2016	49	2016
Provably efficient reinforcement learning with linear function approximation C Jin, Z Yang, Z Wang, MI Jordan Conference on Learning Theory, 2137-2143, 2020	44	2020
On gradient descent ascent for nonconvex-concave minimax problems T Lin, C Jin, MI Jordan arXiv preprint arXiv:1906.00331, 2019	44	2019
Global convergence of non-convex gradient descent for computing matrix squareroot P Jain, C Jin, S Kakade, P Netrapalli Artificial Intelligence and Statistics, 479-488, 2017	39*	2017
Sampling can be faster than optimization YA Ma, Y Chen, C Jin, N Flammarion, MI Jordan Proceedings of the National Academy of Sciences 116 (42), 20881-20885, 2019	33	2019
Stochastic gradient descent escapes saddle points efficiently C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan arXiv preprint arXiv:1902.04811, 2019	31	2019
Dimensionality dependent PAC-Bayes margin bound C Jin, L Wang Advances in neural information processing systems, 1034-1042, 2012	28	2012
On the local minima of the empirical risk C Jin, LT Liu, R Ge, MI Jordan Advances in neural information processing systems, 4896-4905, 2018	26*	2018

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