Data-driven Regularization for Inverse Problems
Abstract
In many applications, we are interested in studying an object of interest x. However, its properties are often not directly measurable. Instead, we only observe y = F(x), where the (known) function F is referred to as the forward mapping. To infer about x from the observation y, we seek an (approximate) inverse mapping x ≈ F−1(y). This process is known as solving the inverse problem. In practice, the inverse mapping F−1 may be ill-defined or discontinuous, which forces us to choose a certain solution from many possible ones based on prior assumptions on x. Such assumptions about x are typically incorporated into the inversion process through regularization. Therefore, the first part of this course focuses on the classical regularization theory of inverse problems. In the second part, we transition to algorithmic approaches for solving inverse problems. The foundation for this lies in the variational formulation of inverse problems, where reconstructions are obtained by minimizing specific functionals. Here, we will explore key optimization principles, discuss their practical implementation, and examine how they contribute to robust reconstructions. Today, classical methods are often modified to incorporate data-driven machine learning techniques, which improve the empirical performance. However, this often comes at the cost of explainability or convergence guarantees. Such properties are critical, for example, in medical applications, where one wants to be confident that reconstructed abnormalities are of physical nature and not phantoms resulting from the method. For these reasons, we discuss a number of machine learning methods for inverse problems, such as unrolling techniques, learned regularizers and plug-and-play methods, and explore their performance guarantees.
Related
- Data-driven Smooth-to-hard Spectral Regularization for General Linear Inverse Problems
- Data-driven stochastic asymptotic regularization for ill-posed inverse problems with a posteriori and heuristic stopping
- Physics-Guided Conditional Flow Matching with Energy Regularization for Robust PDE Inverse Problems
- Structure-Driven Inversion: A New Paradigm for Solving Inverse Problems
- Inverse eigenvalue problem for discrete three-diagonal Schroedinger operator and the continuum limit in Tikhonov regularization approach
BibTeX
@misc{melnyk2026datadriven,
title = {{Data-driven Regularization for Inverse Problems}},
author = {Oleh Melnyk and Sebastian Neumayer},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40048},
eprint = {2610.40048},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40048},
}
RIS
TY - UNPB
TI - Data-driven Regularization for Inverse Problems
AU - Oleh Melnyk
AU - Sebastian Neumayer
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40048
UR - https://arxiv.org/abs/2610.40048
AB - In many applications, we are interested in studying an object of interest x. However, its properties are often not directly measurable. Instead, we only observe y = F(x), where the (known) function F is referred to as the forward mapping. To infer about x from the observation y, we seek an (approximate) inverse mapping x ≈ F^−1(y). This process is known as solving the inverse problem. In practice, the inverse mapping F^−1 may be ill-defined or discontinuous, which forces us to choose a certain solution from many possible ones based on prior assumptions on x. Such assumptions about x are typically incorporated into the inversion process through regularization. Therefore, the first part of this course focuses on the classical regularization theory of inverse problems. In the second part, we transition to algorithmic approaches for solving inverse problems. The foundation for this lies in the variational formulation of inverse problems, where reconstructions are obtained by minimizing specific functionals. Here, we will explore key optimization principles, discuss their practical implementation, and examine how they contribute to robust reconstructions. Today, classical methods are often modified to incorporate data-driven machine learning techniques, which improve the empirical performance. However, this often comes at the cost of explainability or convergence guarantees. Such properties are critical, for example, in medical applications, where one wants to be confident that reconstructed abnormalities are of physical nature and not phantoms resulting from the method. For these reasons, we discuss a number of machine learning methods for inverse problems, such as unrolling techniques, learned regularizers and plug-and-play methods, and explore their performance guarantees.
ER -