Publications

2023

• Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage
  Y. Gui, C. Ma, Y. Zhong
  

• Conformalized Matrix Completion
  Y. Gui, R. F. Barber, C. Ma
  to appear in Neurips, 2023 [Code]
  

• The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
  X. Xu, Y. Shen, Y. Chi, C. Ma
  ICML, 2023

2022

• Jump-Start Reinforcement Learning
  I. Uchendu, T. Xiao, Y. Lu, B. Zhu, M. Yan, J. Simon, M. Bennice, C. Fu, C. Ma, J. Jiao, S Levine, K Hausman
  ICML, 2023

• Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
  Y. Yang, C. Ma
  ICLR, 2023
  

• Optimal Tuning-Free Convex Relaxation for Noisy Matrix Completion
  Y. Yang, C. Ma
  IEEE Transactions on Information Theory, 2023
  

• Pessimism for Offline Linear Contextual Bandits using Lp Confidence Sets
  G. Li, C. Ma, N. Srebro (alphabetical order)
  Neurips, 2022 [Poster]
  

• Optimally Tackling Covariate Shift in RKHS-based Nonparametric Regression
  C. Ma, R. Pathak, M. J. Wainwright
  Annals of Statistics, 2023 [Slides]
  

• A New Similarity Measure for Covariate Shift with Applications to Nonparametric Regression
  R. Pathak, C. Ma, M. J. Wainwright
  ICML, 2022 (long presentation)

2021

• Minimax Off-Policy Evaluation for Multi-Armed Bandits
  C. Ma, B. Zhu, J. Jiao, M. J. Wainwright
  IEEE Transactions on Information Theory, 2022 [Slides]
  

• Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
  P. Rashidinejad, B. Zhu, C. Ma, J. Jiao, S. Russell
  IEEE Transactions on Information Theory, 2022
  accepted in part to Neurips 2021 [Slides]
  

• Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements
  T. Tong, C. Ma, A. Prater-Bennette, E. Tripp, Y. Chi
  Journal of Machine Learning Research, 2022
  accepted in part to AISTATS, 2022

2020

• Spectral Methods for Data Science: A Statistical Perspective
  Y. Chen, Y. Chi, J. Fan, C. Ma (alphabetical order)
  Foundations and Trends® in Machine Learning, 2021

• Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data
  Y. Chen, J. Fan, C. Ma, Y. Yan (alphabetical order)
  Annals of Statistics, 2021
  

• Learning Mixtures of Low-Rank Models
  Y. Chen, C. Ma, H. V. Poor, Y. Chen
  IEEE Transactions on Information Theory, 2021
  

• Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number
  T. Tong, C. Ma, Y. Chi
  IEEE Transactions on Signal Processing, 2021
  

• Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
  T. Tong, C. Ma, Y. Chi
  Journal of Machine Learning Research, 2021
  

2019

• Inference and Uncertainty Quantification for Noisy Matrix Completion
  Y. Chen, J. Fan, C. Ma, Y. Yan (alphabetical order)
  Proceedings of the National Academy of Sciences, 2019 [Slides] [PNAS version]
  

• A Selective Overview of Deep Learning
  J. Fan, C. Ma, Y. Zhong (alphabetical order)
  Statistical Science, 2020
  

• Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
  Y. Chen, Y. Chi, J. Fan, C. Ma, Y. Yan (alphabetical order)
  SIAM Journal on Optimization, 2020 [Slides]
  

• Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
  C. Ma, Y. Li, Y. Chi
  IEEE Transactions on Signal Processing, 2021
  accepted in part to Asilomar 2019

2018

• Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
  Y. Chen, Y. Chi, J. Fan, C. Ma (alphabetical order)
  Mathematical Programming, 2019 [Slides] [MP version]

• Nonconvex Matrix Factorization from Rank-One Measurements
  Y. Li, C. Ma, Y. Chen, Y. Chi
  IEEE Transactions on Information Theory, 2020
  accepted in part to AISTATS, 2019

2017

• Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution
  C. Ma, K. Wang, Y. Chi, Y. Chen
  Foundations of Computational Mathematics, 2020
  accepted in part to ICML 2018 [Slides] [ICML version] [FoCM version]

• Spectral Method and Regularized MLE Are Both Optimal for Top- Ranking
  Y. Chen, J. Fan, C. Ma, K. Wang (alphabetical order)
  Annals of Statistics, 2019 [Slides] [AoS version]

• Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach
  C. Ma, J. Lu, H. Liu
  Journal of the American Statistical Association, 2020 [Slides]
  

2015

• Fast and Flexible Top- Similarity Search on Large Networks
  J. Zhang, J. Tang, C. Ma, H. Tong, Y. Jing, J. Li, W. Luyten, M. Moens
  ACM Transactions on Information Systems, 2017
  accepted in part to KDD, 2015 [KDD version] [TOIS version]