Chi Jin

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyAdresse e-mail validée de cs.berkeley.edu
Praneeth NetrapalliGoogle ResearchAdresse e-mail validée de google.com
Rong GeDuke UniversityAdresse e-mail validée de cs.duke.edu
Sham M KakadeHarvard UniversityAdresse e-mail validée de seas.harvard.edu
Aaron SidfordStanford UniversityAdresse e-mail validée de stanford.edu
Prateek JainGoogle Research IndiaAdresse e-mail validée de google.com
Yang YuanMassachusetts Institute of TechnologyAdresse e-mail validée de cs.cornell.edu
Zeyuan Allen-Zhu （朱澤園）Meta FAIR LabsAdresse e-mail validée de csail.mit.edu
Sebastien BubeckSr Principal Research Manager, ML Foundations group, 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. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityAdresse e-mail validée de princeton.edu
Cameron MuscoAssistant Professor, University of Massachusetts AmherstAdresse e-mail validée de cs.umass.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityAdresse e-mail validée de cmu.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityAdresse e-mail validée de cs.cmu.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 princeton.edu
Yuchen ZhangPrincipal 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

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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	1055	2015
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017	740	2017
Is Q-learning provably efficient? C Jin, Z Allen-Zhu, S Bubeck, MI Jordan Advances in neural information processing systems 31, 2018	628	2018
Provably efficient reinforcement learning with linear function approximation C Jin, Z Yang, Z Wang, MI Jordan Conference on Learning Theory, 2137-2143, 2020	414	2020
No spurious local minima in nonconvex low rank problems: A unified geometric analysis R Ge, C Jin, Y Zheng International Conference on Machine Learning, 1233-1242, 2017	397	2017
On gradient descent ascent for nonconvex-concave minimax problems T Lin, C Jin, M Jordan International Conference on Machine Learning, 6083-6093, 2020	315	2020
What is local optimality in nonconvex-nonconcave minimax optimization? C Jin, P Netrapalli, M Jordan International conference on machine learning, 4880-4889, 2020	280*	2020
Accelerated gradient descent escapes saddle points faster than gradient descent C Jin, P Netrapalli, MI Jordan Conference On Learning Theory, 1042-1085, 2018	236	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	224	2017
Provably efficient exploration in policy optimization Q Cai, Z Yang, C Jin, Z Wang International Conference on Machine Learning, 1283-1294, 2020	183	2020
Near-optimal algorithms for minimax optimization T Lin, C Jin, MI Jordan Conference on Learning Theory, 2738-2779, 2020	174	2020
On nonconvex optimization for machine learning: Gradients, stochasticity, and saddle points C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan Journal of the ACM (JACM) 68 (2), 1-29, 2021	157*	2021
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 29, 2016	150	2016
Stochastic cubic regularization for fast nonconvex optimization N Tripuraneni, M Stern, C Jin, J Regier, MI Jordan Advances in neural information processing systems 31, 2018	148	2018
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	130	2019
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	127	2016
Reward-free exploration for reinforcement learning C Jin, A Krishnamurthy, M Simchowitz, T Yu International Conference on Machine Learning, 4870-4879, 2020	116	2020
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	112*	2016
Provable meta-learning of linear representations N Tripuraneni, C Jin, M Jordan International Conference on Machine Learning, 10434-10443, 2021	102	2021
Provable self-play algorithms for competitive reinforcement learning Y Bai, C Jin International conference on machine learning, 551-560, 2020	101	2020

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