Chi Jin

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerifierad e-postadress på cs.berkeley.edu
Praneeth NetrapalliGoogle ResearchVerifierad e-postadress på google.com
Rong GeDuke UniversityVerifierad e-postadress på cs.duke.edu
Sham M KakadeHarvard UniversityVerifierad e-postadress på seas.harvard.edu
Aaron SidfordStanford UniversityVerifierad e-postadress på stanford.edu
Prateek JainGoogle Research IndiaVerifierad e-postadress på google.com
Yang YuanTsinghua UniversityVerifierad e-postadress på tsinghua.edu.cn
Zeyuan Allen-ZhuMeta AI / FAIR LabsVerifierad e-postadress på csail.mit.edu
Sebastien BubeckOpenAIVerifierad e-postadress på openai.com
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonVerifierad e-postadress på cs.washington.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerifierad e-postadress på princeton.edu
Cameron MuscoAssistant Professor, University of Massachusetts AmherstVerifierad e-postadress på cs.umass.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityVerifierad e-postadress på cmu.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerifierad e-postadress på cs.cmu.edu
Dan GarberAssociate Professor at Technion - Israel Institute of TechnologyVerifierad e-postadress på technion.ac.il
Elad HazanProfessor at Princeton University and Director Google AI PrincetonVerifierad e-postadress på princeton.edu
Yuchen ZhangResearch Scientist at ByteDanceVerifierad e-postadress på microsoft.com
Sivaraman BalakrishnanCarnegie Mellon UniversityVerifierad e-postadress på stat.cmu.edu

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Chi Jin

Assistant Professor, Princeton University

Verifierad e-postadress på princeton.edu - Startsida

Machine Learning Optimization


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	1304	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	1033	2017
Is Q-learning provably efficient? C Jin, Z Allen-Zhu, S Bubeck, MI Jordan Advances in neural information processing systems 31, 2018	1022	2018
Provably efficient reinforcement learning with linear function approximation C Jin, Z Yang, Z Wang, MI Jordan Conference on learning theory, 2137-2143, 2020	887	2020
On gradient descent ascent for nonconvex-concave minimax problems T Lin, C Jin, M Jordan International Conference on Machine Learning, 6083-6093, 2020	620	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	532	2017
What is local optimality in nonconvex-nonconcave minimax optimization? C Jin, P Netrapalli, M Jordan International conference on machine learning, 4880-4889, 2020	480*	2020
Provably efficient exploration in policy optimization Q Cai, Z Yang, C Jin, Z Wang International Conference on Machine Learning, 1283-1294, 2020	328	2020
Near-optimal algorithms for minimax optimization T Lin, C Jin, MI Jordan Conference on Learning Theory, 2738-2779, 2020	314	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	313	2017
Accelerated gradient descent escapes saddle points faster than gradient descent C Jin, P Netrapalli, MI Jordan Conference On Learning Theory, 1042-1085, 2018	296	2018
Reward-free exploration for reinforcement learning C Jin, A Krishnamurthy, M Simchowitz, T Yu International Conference on Machine Learning, 4870-4879, 2020	271	2020
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	267	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	260	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	250*	2021
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	238	2019
Provable meta-learning of linear representations N Tripuraneni, C Jin, M Jordan International Conference on Machine Learning, 10434-10443, 2021	220	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	201	2018
Provable self-play algorithms for competitive reinforcement learning Y Bai, C Jin International conference on machine learning, 551-560, 2020	192	2020
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	191	2016

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