Chi Jin
Chi Jin
Ph.D student, UC Berkeley
Verified email at - 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
How to escape saddle points efficiently
C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan
arXiv preprint arXiv:1703.00887, 2017
No spurious local minima in nonconvex low rank problems: A unified geometric analysis
R Ge, C Jin, Y Zheng
arXiv preprint arXiv:1704.00708, 2017
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
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
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
Accelerated gradient descent escapes saddle points faster than gradient descent
C Jin, P Netrapalli, MI Jordan
arXiv preprint arXiv:1711.10456, 2017
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
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
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
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
Stochastic Cubic Regularization for Fast Nonconvex Optimization
N Tripuraneni, M Stern, C Jin, J Regier, MI Jordan
arXiv preprint arXiv:1711.02838, 2017
Dimensionality dependent PAC-Bayes margin bound
C Jin, L Wang
Advances in neural information processing systems, 1034-1042, 2012
Differentially private data releasing for smooth queries
Z Wang, C Jin, K Fan, J Zhang, J Huang, Y Zhong, L Wang
The Journal of Machine Learning Research 17 (1), 1779-1820, 2016
Is q-learning provably efficient?
C Jin, Z Allen-Zhu, S Bubeck, MI Jordan
Advances in Neural Information Processing Systems, 4864-4874, 2018
Minimizing Nonconvex Population Risk from Rough Empirical Risk
C Jin, LT Liu, R Ge, MI Jordan
arXiv preprint arXiv:1803.09357, 2018
Sampling Can Be Faster Than Optimization
YA Ma, Y Chen, C Jin, N Flammarion, MI Jordan
arXiv preprint arXiv:1811.08413, 2018
Stability and Convergence Trade-off of Iterative Optimization Algorithms
Y Chen, C Jin, B Yu
arXiv preprint arXiv:1804.01619, 2018
On the Local Minima of the Empirical Risk
C Jin, LT Liu, R Ge, MI Jordan
Advances in Neural Information Processing Systems, 4897-4906, 2018
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