Cong Ma
Orcid: 0000-0003-2532-0038Affiliations:
- University of Chicago, Department of Statistics, Chicago, IL, USA
- University of California Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA, USA
- Princeton University, Department of Operations Research and Financial Engineering, Princeton, NJ, USA (PhD 2020)
According to our database1,
Cong Ma
authored at least 40 papers
between 2015 and 2024.
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Online presence:
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on orcid.org
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Bibliography
2024
High-Probability Sample Complexities for Policy Evaluation With Linear Function Approximation.
IEEE Trans. Inf. Theory, August, 2024
CoRR, 2024
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Twelfth International Conference on Learning Representations, 2024
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024
2023
IEEE Trans. Inf. Theory, October, 2023
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization.
CoRR, 2023
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage.
CoRR, 2023
Sharp high-probability sample complexities for policy evaluation with linear function approximation.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the International Conference on Machine Learning, 2023
O(T<sup>-1</sup> Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
2022
IEEE Trans. Inf. Theory, 2022
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements.
J. Mach. Learn. Res., 2022
$O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games.
CoRR, 2022
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent.
CoRR, 2022
CoRR, 2022
A new similarity measure for covariate shift with applications to nonparametric regression.
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the IEEE International Conference on Acoustics, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
Low-Rank Matrix Recovery With Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number.
IEEE Trans. Signal Process., 2021
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing.
IEEE Trans. Signal Process., 2021
IEEE Trans. Inf. Theory, 2021
J. Mach. Learn. Res., 2021
Found. Trends Mach. Learn., 2021
2020
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization.
SIAM J. Optim., 2020
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution.
Found. Comput. Math., 2020
Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data.
CoRR, 2020
2019
Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval.
Math. Program., 2019
2018
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion.
Proceedings of the 35th International Conference on Machine Learning, 2018
2017
ACM Trans. Inf. Syst., 2017
2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015