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Cong Ma 0001
Person information
- affiliation: University of Chicago, Department of Statistics, Chicago, IL, USA
- affiliation: University of California Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA, USA
- affiliation (PhD 2020): Princeton University, Department of Operations Research and Financial Engineering, Princeton, NJ, USA
Other persons with the same name
- Cong Ma — disambiguation page
- Cong Ma 0002
— Chinese Academy of Sciences, Institute of Automation, State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) / formerly, National Laboratory of Pattern Recognition (NLPR), Beijing, China (and 1 more)
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2020 – today
- 2024
- [j15]Gen Li, Weichen Wu
High-Probability Sample Complexities for Policy Evaluation With Linear Function Approximation. IEEE Trans. Inf. Theory 70(8): 5969-5999 (2024) - [c15]Yuepeng Yang, Antares Chen, Lorenzo Orecchia, Cong Ma:
Top-K ranking with a monotone adversary. COLT 2024: 5123-5162 - [c14]Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin:
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift. ICLR 2024 - [c13]Jeonghwan Lee, Cong Ma:
Off-policy estimation with adaptively collected data: the power of online learning. NeurIPS 2024 - [i32]Rong Jiang, Cong Ma:
Batched Nonparametric Contextual Bandits. CoRR abs/2402.17732 (2024) - [i31]Yuepeng Yang, Cong Ma:
Random pairing MLE for estimation of item parameters in Rasch model. CoRR abs/2406.13989 (2024) - [i30]Jeonghwan Lee, Cong Ma:
Off-policy estimation with adaptively collected data: the power of online learning. CoRR abs/2411.12786 (2024) - [i29]Boxin Zhao, Cong Ma, Mladen Kolar:
Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation. CoRR abs/2411.15624 (2024) - 2023
- [j14]Yuepeng Yang
Optimal Tuning-Free Convex Relaxation for Noisy Matrix Completion. IEEE Trans. Inf. Theory 69(10): 6571-6585 (2023) - [c12]Yuepeng Yang, Cong Ma:
O(T-1 Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games. ICLR 2023 - [c11]Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman:
Jump-Start Reinforcement Learning. ICML 2023: 34556-34583 - [c10]Xingyu Xu, Yandi Shen, Yuejie Chi, Cong Ma:
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing. ICML 2023: 38611-38654 - [c9]Yu Gui, Rina Barber, Cong Ma:
Conformalized matrix completion. NeurIPS 2023 - [i28]Xingyu Xu, Yandi Shen, Yuejie Chi, Cong Ma:
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing. CoRR abs/2302.01186 (2023) - [i27]Gen Li, Weichen Wu, Yuejie Chi, Cong Ma, Alessandro Rinaldo, Yuting Wei:
Sharp high-probability sample complexities for policy evaluation with linear function approximation. CoRR abs/2305.19001 (2023) - [i26]Yu Gui, Cong Ma, Yiqiao Zhong:
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage. CoRR abs/2306.03335 (2023) - [i25]Cong Ma, Xingyu Xu, Tian Tong, Yuejie Chi:
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization. CoRR abs/2310.06159 (2023) - [i24]Jiawei Ge, Shange Tang, Jianqing Fan, Cong Ma, Chi Jin:
Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift. CoRR abs/2311.15961 (2023) - 2022
- [j13]Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin E. Tripp, Yuejie Chi:
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements. J. Mach. Learn. Res. 23: 163:1-163:77 (2022) - [j12]Cong Ma
Minimax Off-Policy Evaluation for Multi-Armed Bandits. IEEE Trans. Inf. Theory 68(8): 5314-5339 (2022) - [j11]Paria Rashidinejad, Banghua Zhu
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. IEEE Trans. Inf. Theory 68(12): 8156-8196 (2022) - [c8]Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin E. Tripp, Yuejie Chi:
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Completion. AISTATS 2022: 2607-2617 - [c7]Tian Tong, Cong Ma, Yuejie Chi
Accelerating ILL-Conditioned Robust Low-Rank Tensor Regression. ICASSP 2022: 9072-9076 - [c6]Reese Pathak, Cong Ma, Martin J. Wainwright:
A new similarity measure for covariate shift with applications to nonparametric regression. ICML 2022: 17517-17530 - [i23]Reese Pathak, Cong Ma, Martin J. Wainwright:
A new similarity measure for covariate shift with applications to nonparametric regression. CoRR abs/2202.02837 (2022) - [i22]Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman:
Jump-Start Reinforcement Learning. CoRR abs/2204.02372 (2022) - [i21]Cong Ma, Reese Pathak, Martin J. Wainwright:
Optimally tackling covariate shift in RKHS-based nonparametric regression. CoRR abs/2205.02986 (2022) - [i20]Harry Dong, Tian Tong, Cong Ma, Yuejie Chi
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent. CoRR abs/2206.09109 (2022) - [i19]Yuepeng Yang, Cong Ma:
Optimal tuning-free convex relaxation for noisy matrix completion. CoRR abs/2207.05802 (2022) - [i18]Yuepeng Yang, Cong Ma:
$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games. CoRR abs/2209.12430 (2022) - 2021
- [j10]Yuxin Chen
Spectral Methods for Data Science: A Statistical Perspective. Found. Trends Mach. Learn. 14(5): 566-806 (2021) - [j9]Tian Tong, Cong Ma, Yuejie Chi:
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent. J. Mach. Learn. Res. 22: 150:1-150:63 (2021) - [j8]Yuanxin Li, Cong Ma, Yuxin Chen
Nonconvex Matrix Factorization From Rank-One Measurements. IEEE Trans. Inf. Theory 67(3): 1928-1950 (2021) - [j7]Yanxi Chen
Learning Mixtures of Low-Rank Models. IEEE Trans. Inf. Theory 67(7): 4613-4636 (2021) - [j6]Cong Ma
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing. IEEE Trans. Signal Process. 69: 867-877 (2021) - [j5]Tian Tong, Cong Ma
Low-Rank Matrix Recovery With Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number. IEEE Trans. Signal Process. 69: 2396-2409 (2021) - [c5]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. NeurIPS 2021: 11702-11716 - [i17]Cong Ma, Yuanxin Li, Yuejie Chi:
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing. CoRR abs/2101.05113 (2021) - [i16]Cong Ma, Banghua Zhu, Jiantao Jiao, Martin J. Wainwright:
Minimax Off-Policy Evaluation for Multi-Armed Bandits. CoRR abs/2101.07781 (2021) - [i15]Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, Stuart Russell:
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism. CoRR abs/2103.12021 (2021) - [i14]Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin E. Tripp, Yuejie Chi:
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements. CoRR abs/2104.14526 (2021) - 2020
- [j4]Cong Ma, Kaizheng Wang
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution. Found. Comput. Math. 20(3): 451-632 (2020) - [j3]Yuxin Chen
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization. SIAM J. Optim. 30(4): 3098-3121 (2020) - [i13]Yuxin Chen, Jianqing Fan, Cong Ma, Yuling Yan:
Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data. CoRR abs/2001.05484 (2020) - [i12]Tian Tong, Cong Ma, Yuejie Chi:
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent. CoRR abs/2005.08898 (2020) - [i11]Yanxi Chen, Cong Ma, H. Vincent Poor, Yuxin Chen:
Learning Mixtures of Low-Rank Models. CoRR abs/2009.11282 (2020) - [i10]Tian Tong, Cong Ma, Yuejie Chi:
Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number. CoRR abs/2010.13364 (2020) - [i9]Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma:
Spectral Methods for Data Science: A Statistical Perspective. CoRR abs/2012.08496 (2020)
2010 – 2019
- 2019
- [j2]Yuxin Chen
Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval. Math. Program. 176(1-2): 5-37 (2019) - [c4]Cong Ma, Yuanxin Li, Yuejie Chi
Beyond Procrustes: Balancing-free Gradient Descent for Asymmetric Low-Rank Matrix Sensing. ACSSC 2019: 721-725 - [c3]Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi:
Nonconvex Matrix Factorization from Rank-One Measurements. AISTATS 2019: 1496-1505 - [i8]Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma, Yuling Yan:
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization. CoRR abs/1902.07698 (2019) - [i7]Jianqing Fan, Cong Ma, Yiqiao Zhong:
A Selective Overview of Deep Learning. CoRR abs/1904.05526 (2019) - [i6]Yuxin Chen, Jianqing Fan, Cong Ma, Yuling Yan:
Inference and Uncertainty Quantification for Noisy Matrix Completion. CoRR abs/1906.04159 (2019) - 2018
- [c2]Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen:
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion. ICML 2018: 3351-3360 - [i5]Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi:
Nonconvex Matrix Factorization from Rank-One Measurements. CoRR abs/1802.06286 (2018) - [i4]Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma:
Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval. CoRR abs/1803.07726 (2018) - 2017
- [j1]Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong
Fast and Flexible Top-k Similarity Search on Large Networks. ACM Trans. Inf. Syst. 36(2): 13:1-13:30 (2017) - [i3]Yuxin Chen, Jianqing Fan, Cong Ma, Kaizheng Wang:
Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking. CoRR abs/1707.09971 (2017) - [i2]Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen:
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion and Blind Deconvolution. CoRR abs/1711.10467 (2017) - 2015
- [c1]Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong
Panther: Fast Top-k Similarity Search on Large Networks. KDD 2015: 1445-1454 - [i1]Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong, Yu Jing, Juan-Zi Li:
Panther: Fast Top-k Similarity Search in Large Networks. CoRR abs/1504.02577 (2015)
Coauthor Index
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