Publications by topics

Reinforcement learning

• Learning to Answer from Correct Demonstrations
  N. Joshi, G. Li, S. Bhandari, S. P. Kasiviswanathan, C. Ma, N. Srebro
  ICLR, 2026 [Slides]

• The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
  R. Jiang, C. Ma
  [Slides]

• Batched Nonparametric Contextual Bandits
  R. Jiang, C. Ma
  IEEE Transactions on Information Theory, 2025 [Slides]

• Off-Policy Estimation with Adaptively Collected Data: The Power of Online Learning
  J. Lee, C. Ma
  NeurIPS, 2024

• High-Probability Sample Complexities for Policy Evaluation with Linear Function Approximation
  G. Li, W. Wu, Y. Chi, C. Ma, A. Rinaldo, Y. Wei
  IEEE Transactions on Information Theory, 2024

• 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

• \(O(1/T)\) Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
  Y. Yang, C. Ma
  ICLR, 2023

• Pessimism for Offline Linear Contextual Bandits using \(\ell_{p}\) Confidence Sets
  G. Li, C. Ma, N. Srebro
  NeurIPS, 2022 [Poster]

• 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]

Transfer learning

• Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation
  B. Zhao, C. Ma, M. Kolar
  Journal of the American Statistical Association, 2025

• Distributionally Robust Risk Evaluation with an Isotonic Constraint
  Y. Gui, R. F. Barber, C. Ma
  

• On the Design-Dependent Suboptimality of the Lasso
  R. Pathak, C. Ma
  [Code]

• Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shift
  J. Ge, S. Tang, J. Fan, C. Ma, C. Jin
  ICLR, 2024

• 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 (RP and CM contributed equally)
  ICML, 2022 (long presentation)

Multi-modal learning

• Multi-Modal Contrastive Learning Adapts to Intrinsic Dimensions of Shared Latent Variables
  Y. Gui, C. Ma, Z. Ma
  NeurIPS, 2025

• IndiSeek Learns Information-Guided Disentangled Representations
  Y. Gui, C. Ma, Z. Ma
  

• Estimating Shared Subspace with AJIVE: The Power and Limitation of Multiple Data Matrices
  Y. Yang, C. Ma
  [Slides]

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

Low-rank matrix recovery

• Conformalized Matrix Completion
  Y. Gui, R. F. Barber, C. Ma
  NeurIPS, 2023 [Code]

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

• 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

• 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]

• 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

• 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]

• Nonconvex Matrix Factorization from Rank-One Measurements
  Y. Li, C. Ma, Y. Chen, Y. Chi
  IEEE Transactions on Information Theory, 2021, accepted in part to AISTATS, 2019

• 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]

Scaled gradient descent

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

• Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent
  H. Dong, T. Tong, C. Ma, Y. Chi
  Information and Inference: A Journal of the IMA, 2023

• 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

• 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

Ranking

• Random Pairing MLE for Estimation of Item Parameters in Rasch Model
  Y. Yang, C. Ma
  Journal of the American Statistical Association, 2025

• Top-K Ranking with a Monotone Adversary
  Y. Yang, A. Chen, L. Orecchia, C. Ma
  COLT, 2024

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

Others

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

• Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach
  C. Ma, J. Lu, H. Liu
  Journal of American Statistical Association, 2021 [Slides]

• Fast and Flexible Top-K 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

• Modern Data Modeling: Cross-Fertilization of the Two Cultures
  J. Fan, C. Ma, K. Wang, Z. Zhu (alphabetical order)
  Observational Studies, 2021

• Comment on “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”
  J. Fan, C. Ma, K. Wang (alphabetical order)
  Journal of the American Statistical Association, 2020