The solution of inverse problems is of fundamental interest in medical and astronomical
imaging, geophysics as well as engineering and life sciences. Recent advances were made …

In inverse problems, many conditional generative models approximate the posterior
measure by minimizing a distance between the joint measure and its learned approximation …

We propose conditional flows of the maximum mean discrepancy (MMD) with the negative
distance kernel for posterior sampling and conditional generative modeling. This MMD …

Bayesian inference usually requires running potentially costly inference procedures
separately for every new observation. In contrast, the idea of amortized Bayesian inference …

This paper provides an overview of current approaches for solving inverse problems in
imaging using variational methods and machine learning. A special focus lies on point …

Bayesian methods for solving inverse problems are a powerful alternative to classical
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …

Bayesian methods for solving inverse problems are a powerful alternative to classical
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …

In inverse problems, many conditional generative models approximate the posterior
measure by minimizing a distance between the joint measure and its learned approximation …

We investigate sample-based learning of conditional distributions on multi-dimensional unit
boxes, allowing for different dimensions of the feature and target spaces. Our approach …

In this work, we explore the theoretical properties of conditional deep generative models
under the statistical framework of distribution regression where the response variable lies in …