| 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 | 980 | 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 | 670 | 2017 |
| Is Q-learning provably efficient? C Jin, Z Allen-Zhu, S Bubeck, MI Jordan Advances in neural information processing systems 31, 2018 | 533 | 2018 |
| 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 | 378 | 2017 |
| Provably efficient reinforcement learning with linear function approximation C Jin, Z Yang, Z Wang, MI Jordan Conference on Learning Theory, 2137-2143, 2020 | 340 | 2020 |
| On gradient descent ascent for nonconvex-concave minimax problems T Lin, C Jin, M Jordan International Conference on Machine Learning, 6083-6093, 2020 | 246 | 2020 |
| What is local optimality in nonconvex-nonconcave minimax optimization? C Jin, P Netrapalli, M Jordan International conference on machine learning, 4880-4889, 2020 | 234* | 2020 |
| Accelerated gradient descent escapes saddle points faster than gradient descent C Jin, P Netrapalli, MI Jordan Conference On Learning Theory, 1042-1085, 2018 | 210 | 2018 |
| 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 | 196 | 2017 |
| Provably efficient exploration in policy optimization Q Cai, Z Yang, C Jin, Z Wang International Conference on Machine Learning, 1283-1294, 2020 | 155 | 2020 |
| Near-optimal algorithms for minimax optimization T Lin, C Jin, MI Jordan Conference on Learning Theory, 2738-2779, 2020 | 137 | 2020 |
| 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 | 136 | 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 | 134 | 2016 |
| 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 | 128* | 2021 |
| 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 | 122 | 2016 |
| Faster eigenvector computation via shift-and-invert preconditioning D Garber, E Hazan, C Jin, C Musco, P Netrapalli, A Sidford International Conference on Machine Learning, 2626-2634, 2016 | 110* | 2016 |
| 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 | 107 | 2019 |
| Reward-free exploration for reinforcement learning C Jin, A Krishnamurthy, M Simchowitz, T Yu International Conference on Machine Learning, 4870-4879, 2020 | 95 | 2020 |
| Provable efficient online matrix completion via non-convex stochastic gradient descent C Jin, SM Kakade, P Netrapalli Advances in Neural Information Processing Systems 29, 2016 | 91 | 2016 |
| Provable self-play algorithms for competitive reinforcement learning Y Bai, C Jin International conference on machine learning, 551-560, 2020 | 86 | 2020 |
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeleycs.berkeley.edu의 이메일 확인됨
