Publications

Selected Publications

[See All Publications]

• J. Li^★, J. Wang, R. K. W. Wong and K. C. G. Chan. (2024) "A Pairwise Pseudo-likelihood Approach for Matrix Completion with Informative Missingness". Advances in Neural Information Processing Systems (NeurIPS).
  Spotlight
  [abstract]

• J. Wang, Z. Qi and R. K. W. Wong. (2024) "A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models". International Conference on Machine Learning (ICML).
  [abstract] [proceedings] [arXiv]

• Y. Zhou^★, R. K. W. Wong and K. He. (2024+) "Broadcasted Nonparametric Tensor Regression". Journal of the Royal Statistical Society: Series B.
  [abstract] [journal] [arXiv] [code]

• J. Wang^★, Z. Qi and R. K. W. Wong. (2023) "Projected State-action Balancing Weights for Offline Reinforcement Learning". The Annals of Statistics, 51(4), 1639-1665.
  [abstract] [journal] [arXiv]

• S. Roy^★, R. K. W. Wong and Y. Ni. (2023) "Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data". Advances in Neural Information Processing Systems (NeurIPS).
  NeurIPS Scholar Award (S. Roy)
  [abstract] [proceedings] [arXiv]

• R. Miao^★, X. Zhang and R. K. W. Wong. (2023) "A Wavelet-Based Independence Test for Functional Data with an Application to MEG Functional Connectivity". Journal of the American Statistical Association, 118(543), 1876-1889.
  ICSA Student Paper Award (R. Miao)
  [abstract] [journal] [arXiv] [code]

• J. Li^★, T. V. Nguyen, C. Hegde and R. K. W. Wong. (2023) "Implicit Regularization for Group Sparsity". International Conference on Learning Representations (ICLR).
  [abstract] [proceedings] [arXiv] [code]

• J. Wang^★, R. K. W. Wong and X. Zhang. (2022) "Low-Rank Covariance Function Estimation for Multidimensional Functional Data". Journal of the American Statistical Association, 117(538), 809-822.
  ASA Section on Nonparametric Statistics Student Paper Award (J. Wang)
  [abstract] [journal] [arXiv] [code]

• J. Li^★, T. V. Nguyen, C. Hegde and R. K. W. Wong. (2021) "Implicit Sparse Regularization: The Impact of Depth and Early Stopping". Advances in Neural Information Processing Systems (NeurIPS).
  [abstract] [proceedings] [arXiv] [code]

• J. Wang^★, R. K. W. Wong, X. Mao and K. C. G. Chan. (2021) "Matrix Completion with Model-free Weighting". International Conference on Machine Learning (ICML).
  [abstract] [proceedings] [arXiv] [code]

• T. V. Nguyen^★, R. K. W. Wong and C. Hegde. (2021) "Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis". IEEE Transactions on Information Theory, 67(7), 4669-4692.
  [abstract] [journal] [arXiv]

• W. Liu, X. Mao and R. K. W. Wong. (2020) "Median Matrix Completion: from Embarrassment to Optimality". International Conference on Machine Learning (ICML).
  [abstract] [proceedings] [arXiv]

• T. V. Nguyen^★, R. K. W. Wong and C. Hegde. (2019) "Provably Accurate Double-Sparse Coding". Journal of Machine Learning Research, 20(141), 1-43.
  [abstract] [journal] [arXiv]

• R. K. W. Wong, Y. Li and Z. Zhu. (2019) "Partially Linear Functional Additive Models for Multivariate Functional Data". Journal of the American Statistical Association, 114(525), 406-418.
  [abstract] [journal] [supplement]

• X. Mao^★, S. X. Chen and R. K. W. Wong. (2019) "Matrix Completion with Covariate Information". Journal of the American Statistical Association, 114(525), 198-210.
  ICSA Student Paper Award (X. Mao)
  [abstract] [journal] [supplement]

• R. K. W. Wong and X. Zhang. (2019) "Nonparametric Operator-Regularized Covariance Function Estimation for Functional Data". Computational Statistics & Data Analysis, 131, Special Issue on High-dimensional and Functional Data Analysis, 131-144.
  [abstract] [journal] [arXiv] [supplement] [code]

• R. K. W. Wong and K. C. G. Chan. (2018) "Kernel-based Covariate Functional Balancing for Observational Studies". Biometrika, 105(1), 199-213.
  [abstract] [journal] [PDF] [supplement] [code]

• R. K. W. Wong and T. C. M. Lee. (2017) "Matrix Completion with Noisy Entries and Outliers". Journal of Machine Learning Research, 18(147), 1-25.
  [abstract] [journal] [arXiv]

• R. K. W. Wong, C. B. Storlie and T. C. M. Lee. (2017) "A Frequentist Approach to Computer Model Calibration". Journal of the Royal Statistical Society: Series B, 79(2), 635-648.
  [abstract] [journal] [arXiv] [supplement] [code]

• R. K. W. Wong, T. C. M. Lee, D. Paul, J. Peng and for the Alzheimer's Disease Neuroimaging Initiative. (2016) "Fiber Direction Estimation, Smoothing and Tracking in Diffusion MRI". The Annals of Applied Statistics, 10(3), 1137-1156.
  Discussion Paper
  [abstract] [journal] [arXiv] [PDF] [supplement]

• R. K. W. Wong, V. L. Kashyap, T. C. M. Lee and D. A. van Dyk. (2016) "Detecting Abrupt Changes in the Spectra of High-energy Astrophysical Sources". The Annals of Applied Statistics, 10(2), 1107-1134.
  [abstract] [journal] [arXiv] [PDF] [code]