Chi Jin
Chi Jin
Ph.D student, UC Berkeley
Verified email at berkeley.edu - Homepage
TitleCited byYear
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
5742015
How to escape saddle points efficiently
C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
2882017
No spurious local minima in nonconvex low rank problems: A unified geometric analysis
R Ge, C Jin, Y Zheng
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
1982017
Accelerated gradient descent escapes saddle points faster than gradient descent
C Jin, P Netrapalli, MI Jordan
arXiv preprint arXiv:1711.10456, 2017
912017
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
87*2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning.
D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford
ICML, 2626-2634, 2016
81*2016
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
782017
Is q-learning provably efficient?
C Jin, Z Allen-Zhu, S Bubeck, MI Jordan
Advances in Neural Information Processing Systems, 4863-4873, 2018
662018
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
612016
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
592018
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
552016
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
372016
Global convergence of non-convex gradient descent for computing matrix squareroot
P Jain, C Jin, SM Kakade, P Netrapalli
arXiv preprint arXiv:1507.05854, 2015
33*2015
Dimensionality dependent PAC-Bayes margin bound
C Jin, L Wang
Advances in Neural Information Processing Systems, 1034-1042, 2012
222012
Minmax optimization: Stable limit points of gradient descent ascent are locally optimal
C Jin, P Netrapalli, MI Jordan
arXiv preprint arXiv:1902.00618, 2019
212019
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
21*2018
Stochastic gradient descent escapes saddle points efficiently
C Jin, P Netrapalli, R Ge, SM Kakade, MI Jordan
arXiv preprint arXiv:1902.04811, 2019
162019
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
152019
On gradient descent ascent for nonconvex-concave minimax problems
T Lin, C Jin, MI Jordan
arXiv preprint arXiv:1906.00331, 2019
112019
Provably efficient reinforcement learning with linear function approximation
C Jin, Z Yang, Z Wang, MI Jordan
arXiv preprint arXiv:1907.05388, 2019
92019
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Articles 1–20