21 citations · 74 across the 15 of their papers we have counts for
18 papers
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization
Emma Hart, Julianne Chung, Matthias Chung
In this work, we describe a new data-driven approach for inverse problems that exploits technologies from machine learning, in particular autoencoder network structures. We conside…
Paired Autoencoders for Likelihood-free Estimation in Inverse Problems
Matthias Chung, Emma Hart, Julianne Chung +2
We consider the solution of nonlinear inverse problems where the forward problem is a discretization of a partial differential equation. Such problems are notoriously difficult to…
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient goal-oriented uncertainty quantification for inverse problems. Contrary…
The Variable Projected Augmented Lagrangian Method
Matthias Chung, Rosemary Renaut
Inference by means of mathematical modeling from a collection of observations remains a crucial tool for scientific discovery and is ubiquitous in application areas such as signal…
Efficient learning methods for large-scale optimal inversion design
Julianne Chung, Matthias Chung, Silvia Gazzola +1
In this work, we investigate various approaches that use learning from training data to solve inverse problems, following a bi-level learning approach. We consider a general framew…
Least-Squares Finite Element Method for Ordinary Differential Equations
Matthias Chung, Justin Krueger, Honghu Liu
We consider the least-squares finite element method (lsfem) for systems of nonlinear ordinary differential equations and establish an optimal error estimate for this method when pi…
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
Elizabeth Newman, Julianne Chung, Matthias Chung +1
Deep neural networks (DNNs) have shown their success as high-dimensional function approximators in many applications; however, training DNNs can be challenging in general. DNN trai…
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning ap…
Sampled Limited Memory Methods for Massive Linear Inverse Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to ne…
Sampled Tikhonov Regularization for Large Linear Inverse Problems
J. Tanner Slagel, Julianne Chung, Matthias Chung +2
In this paper, we investigate iterative methods that are based on sampling of the data for computing Tikhonov-regularized solutions. We focus on very large inverse problems where a…
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes
M. Chung, M. Binois, R. B. Gramacy +3
Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control,…
Optimal Experimental Design for Constrained Inverse Problems
Lars Ruthotto, Julianne Chung, Matthias Chung
In this paper, we address the challenging problem of optimal experimental design (OED) of constrained inverse problems. We consider two OED formulations that allow reducing the exp…
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant c…
Optimal regularized inverse matrices for inverse problems
Julianne Chung, Matthias Chung
In this paper, we consider optimal low-rank regularized inverse matrix approximations and their applications to inverse problems. We give an explicit solution to a generalized rank…
Robust Parameter Estimation for Biological Systems: A Study on the Dynamics of Microbial Communities
Matthias Chung, Justin Krueger, Mihai Pop
Interest in the study of in-host microbial communities has increased in recent years due to our improved understanding of the communities' significant role in host health. As a res…
Simultaneous Source for non-uniform data variance and missing data
Eldad Haber, Mathias Chung
The use of simultaneous sources in geophysical inverse problems has revolutionized the ability to deal with large scale data sets that are obtained from multiple source experiments…
An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices
Julianne Chung, Matthias Chung
Standard regularization methods that are used to compute solutions to ill-posed inverse problems require knowledge of the forward model. In many real-life applications, the forward…
Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects
Qi Long, Matthias Chung, Carlos S. Moreno +1
In biomedical studies it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subje…