Publications by topics

Transfer learning

• 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)

Reinforcement learning

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

• Batched Nonparametric Contextual Bandits
  R. Jiang, C. Ma
  [Slides]
  

• Sharp High-Probability Sample Complexities for Policy Evaluation with Linear Function Approximation
  G. Li, W. Wu, Y. Chi, C. Ma, A. Rinaldo, Y. Wei
  to appear in 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

• 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 Confidence Sets
  G. Li, C. Ma, N. Srebro (alphabetical order)
  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]
  

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
  

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

• 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]

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
  

• 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]

• 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, 2020
  accepted in part to AISTATS, 2019

Others

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

• 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 the American Statistical Association, 2020 [Slides]
  

• 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