289 citations · 1.2k across the 72 of their papers we have counts for
9 papers · 1 filter
Functional Linear Regression with Mixed Predictors
Daren Wang, Zifeng Zhao, Yi Yu +1
We study a functional linear regression model that deals with functional responses and allows for both functional covariates and high-dimensional vector covariates. The proposed mo…
Model Adaptation for Inverse Problems in Imaging
Davis Gilton, Gregory Ongie, Rebecca Willett
Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging. These networks are typically trained using a forward mod…
Functional Autoregressive Processes in Reproducing Kernel Hilbert Spaces
Daren Wang, Zifeng Zhao, Rebecca Willett +1
We study the estimation and prediction of functional autoregressive~(FAR) processes, a statistical tool for modeling functional time series data. Due to the infinite-dimensional na…
Localizing Changes in High-Dimensional Regression Models
Alessandro Rinaldo, Daren Wang, Qin Wen +2
This paper addresses the problem of localizing change points in high-dimensional linear regression models with piecewise constant regression coefficients. We develop a dynamic prog…
Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes
Daren Wang, Yi Yu, Rebecca Willett
High-dimensional self-exciting point processes have been widely used in many application areas to model discrete event data in which past and current events affect the likelihood o…
Deep Learning Techniques for Inverse Problems in Imaging
Gregory Ongie, Ajil Jalal, Christopher A. Metzler +3
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central pre…