30 citations · 110 across the 21 of their papers we have counts for
22 papers
Prediction of Tropical Pacific Rain Rates with Over-parameterized Neural Networks
Hojun You, Jiayi Wang, Raymond K. W. Wong +3
The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study…
Flexible Functional Treatment Effect Estimation
Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang +1
We study treatment effect estimation with functional treatments where the average potential outcome functional is a function of functions, in contrast to continuous treatment effec…
Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior
Shuoli Chen, Kejun He, Shiyuan He +2
Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates…
Implicit Regularization for Group Sparsity
Jiangyuan Li, Thanh V. Nguyen, Chinmay Hegde +1
We study the implicit regularization of gradient descent towards structured sparsity via a novel neural reparameterization, which we call a diagonally grouped linear neural network…
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling
Zhenyu Wei, Raymond K. W. Wong, Thomas C. M. Lee
In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity. Successful examples include sign…
A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae
Mikhail M. Meskhi, Noah E. Wolfe, Zhenyu Dai +4
Understanding how matter behaves at the highest densities and temperatures is a major open problem in both nuclear physics and relativistic astrophysics. This physics is often enca…
Projected State-action Balancing Weights for Offline Reinforcement Learning
Jiayi Wang, Zhengling Qi, Raymond K. W. Wong
Offline policy evaluation (OPE) is considered a fundamental and challenging problem in reinforcement learning (RL). This paper focuses on the value estimation of a target policy ba…
Estimation of Partially Conditional Average Treatment Effect by Hybrid Kernel-covariate Balancing
Jiayi Wang, Raymond K. W. Wong, Shu Yang +1
We study nonparametric estimation for the partially conditional average treatment effect, defined as the treatment effect function over an interested subset of confounders. We prop…
A Wavelet-Based Independence Test for Functional Data with an Application to MEG Functional Connectivity
Rui Miao, Xiaoke Zhang, Raymond K. W. Wong
Measuring and testing the dependency between multiple random functions is often an important task in functional data analysis. In the literature, a model-based method relies on a m…
Low-Rank Covariance Function Estimation for Multidimensional Functional Data
Jiayi Wang, Raymond K. W. Wong, Xiaoke Zhang
Multidimensional function data arise from many fields nowadays. The covariance function plays an important role in the analysis of such increasingly common data. In this paper, we…
Broadcasted Nonparametric Tensor Regression
Ya Zhou, Raymond K. W. Wong, Kejun He
We propose a novel use of a broadcasting operation, which distributes univariate functions to all entries of the tensor covariate, to model the nonlinearity in tensor regression no…
Median Matrix Completion: from Embarrassment to Optimality
Weidong Liu, Xiaojun Mao, Raymond K. W. Wong
In this paper, we consider matrix completion with absolute deviation loss and obtain an estimator of the median matrix. Despite several appealing properties of median, the non-smoo…
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
A remarkable recent discovery in machine learning has been that deep neural networks can achieve impressive performance (in terms of both lower training error and higher generaliza…
Adjusting for Spatial Effects in Genomic Prediction
Xiaojun Mao, Somak Dutta, Raymond K. W. Wong +1
This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect ph…
Matrix Completion under Low-Rank Missing Mechanism
Xiaojun Mao, Raymond K. W. Wong, Song Xi Chen
Matrix completion is a modern missing data problem where both the missing structure and the underlying parameter are high dimensional. Although missing structure is a key component…
Network estimation via graphon with node features
Yi Su, Raymond K. W. Wong, Thomas C. M. Lee
Estimating the probabilities of linkages in a network has gained increasing interest in recent years. One popular model for network analysis is the exchangeable graph model (ExGM)…
Autoencoders Learn Generative Linear Models
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
We provide a series of results for unsupervised learning with autoencoders. Specifically, we study shallow two-layer autoencoder architectures with shared weights. We focus on thre…
Provably Accurate Double-Sparse Coding
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
Sparse coding is a crucial subroutine in algorithms for various signal processing, deep learning, and other machine learning applications. The central goal is to learn an overcompl…
Nonparametric Operator-Regularized Covariance Function Estimation for Functional Data
Raymond K. W. Wong, Xiaoke Zhang
In functional data analysis (FDA), covariance function is fundamental not only as a critical quantity for understanding elementary aspects of functional data but also as an indispe…
Matrix Completion with Noisy Entries and Outliers
Raymond K. W. Wong, Thomas C. M. Lee
This paper considers the problem of matrix completion when the observed entries are noisy and contain outliers. It begins with introducing a new optimization criterion for which th…
A Frequentist Approach to Computer Model Calibration
Raymond K. W. Wong, Curtis B. Storlie, Thomas C. M. Lee
This paper considers the computer model calibration problem and provides a general frequentist solution. Under the proposed framework, the data model is semi-parametric with a nonp…
Fiber Direction Estimation, Smoothing and Tracking in Diffusion MRI
Raymond K. W. Wong, Thomas C. M. Lee, Debashis Paul +2
Diffusion magnetic resonance imaging is an imaging technology designed to probe anatomical architectures of biological samples in an in vivo and non-invasive manner through measuri…