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Chi Jin
Chi Jin
Assistant Professor, Princeton University
Подтвержден адрес электронной почты в домене princeton.edu - Главная страница
Название
Процитировано
Процитировано
Год
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
13032015
How to escape saddle points efficiently
C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan
International conference on machine learning, 1724-1732, 2017
10312017
Is Q-learning provably efficient?
C Jin, Z Allen-Zhu, S Bubeck, MI Jordan
Advances in neural information processing systems 31, 2018
10162018
Provably efficient reinforcement learning with linear function approximation
C Jin, Z Yang, Z Wang, MI Jordan
Conference on learning theory, 2137-2143, 2020
8812020
On gradient descent ascent for nonconvex-concave minimax problems
T Lin, C Jin, M Jordan
International Conference on Machine Learning, 6083-6093, 2020
6202020
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
5302017
What is local optimality in nonconvex-nonconcave minimax optimization?
C Jin, P Netrapalli, M Jordan
International conference on machine learning, 4880-4889, 2020
481*2020
Provably efficient exploration in policy optimization
Q Cai, Z Yang, C Jin, Z Wang
International Conference on Machine Learning, 1283-1294, 2020
3242020
Near-optimal algorithms for minimax optimization
T Lin, C Jin, MI Jordan
Conference on Learning Theory, 2738-2779, 2020
3152020
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
3132017
Accelerated gradient descent escapes saddle points faster than gradient descent
C Jin, P Netrapalli, MI Jordan
Conference On Learning Theory, 1042-1085, 2018
2972018
Reward-free exploration for reinforcement learning
C Jin, A Krishnamurthy, M Simchowitz, T Yu
International Conference on Machine Learning, 4870-4879, 2020
2652020
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
2642021
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
2582020
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
2352019
Provable meta-learning of linear representations
N Tripuraneni, C Jin, M Jordan
International Conference on Machine Learning, 10434-10443, 2021
2172021
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
2022018
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
1902016
Provable self-play algorithms for competitive reinforcement learning
Y Bai, C Jin
International conference on machine learning, 551-560, 2020
1892020
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