Chi Jin

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyDirección de correo verificada de cs.berkeley.edu
Praneeth NetrapalliGoogle ResearchDirección de correo verificada de google.com
Rong GeDuke UniversityDirección de correo verificada de cs.duke.edu
Sham M KakadeHarvard UniversityDirección de correo verificada de seas.harvard.edu
Aaron SidfordStanford UniversityDirección de correo verificada de stanford.edu
Prateek JainGoogle Research IndiaDirección de correo verificada de google.com
Yang YuanTsinghua UniversityDirección de correo verificada de tsinghua.edu.cn
Zeyuan Allen-ZhuMeta AI / FAIR LabsDirección de correo verificada de csail.mit.edu
Sebastien BubeckOpenAIDirección de correo verificada de openai.com
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonDirección de correo verificada de cs.washington.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityDirección de correo verificada de princeton.edu
Cameron MuscoAssistant Professor, University of Massachusetts AmherstDirección de correo verificada de cs.umass.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityDirección de correo verificada de cs.cmu.edu
Dan GarberAssociate Professor at Technion - Israel Institute of TechnologyDirección de correo verificada de technion.ac.il
Elad HazanProfessor at Princeton University and Director Google AI PrincetonDirección de correo verificada de princeton.edu
Yuchen ZhangResearch Scientist at ByteDanceDirección de correo verificada de microsoft.com
Sivaraman BalakrishnanCarnegie Mellon UniversityDirección de correo verificada de stat.cmu.edu

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Chi Jin

Assistant Professor, Princeton University

Dirección de correo verificada de princeton.edu - Página principal

Machine Learning Optimization


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	1282	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	1013	2017
Is Q-learning provably efficient? C Jin, Z Allen-Zhu, S Bubeck, MI Jordan Advances in neural information processing systems 31, 2018	994	2018
Provably efficient reinforcement learning with linear function approximation C Jin, Z Yang, Z Wang, MI Jordan Conference on learning theory, 2137-2143, 2020	855	2020
On gradient descent ascent for nonconvex-concave minimax problems T Lin, C Jin, M Jordan International Conference on Machine Learning, 6083-6093, 2020	592	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	521	2017
What is local optimality in nonconvex-nonconcave minimax optimization? C Jin, P Netrapalli, M Jordan International conference on machine learning, 4880-4889, 2020	468*	2020
Provably efficient exploration in policy optimization Q Cai, Z Yang, C Jin, Z Wang International Conference on Machine Learning, 1283-1294, 2020	316	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	305	2017
Near-optimal algorithms for minimax optimization T Lin, C Jin, MI Jordan Conference on Learning Theory, 2738-2779, 2020	301	2020
Accelerated gradient descent escapes saddle points faster than gradient descent C Jin, P Netrapalli, MI Jordan Conference On Learning Theory, 1042-1085, 2018	291	2018
Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms C Jin, Q Liu, S Miryoosefi Advances in neural information processing systems 34, 13406-13418, 2021	258	2021
Reward-free exploration for reinforcement learning C Jin, A Krishnamurthy, M Simchowitz, T Yu International Conference on Machine Learning, 4870-4879, 2020	258	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	243*	2021
On the theory of transfer learning: The importance of task diversity N Tripuraneni, M Jordan, C Jin Advances in neural information processing systems 33, 7852-7862, 2020	242	2020
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	227	2019
Provable meta-learning of linear representations N Tripuraneni, C Jin, M Jordan International Conference on Machine Learning, 10434-10443, 2021	205	2021
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	197	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 29, 2016	188	2016
Provable self-play algorithms for competitive reinforcement learning Y Bai, C Jin International conference on machine learning, 551-560, 2020	187	2020

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