Representing a manifold of very high-dimensional data with generative models has been
shown to be computationally efficient in practice. However, this requires that the data …

In order to sample from an unnormalized probability density function, we propose to
combine continuous normalizing flows (CNFs) with rejection-resampling steps based on …

We develop an algorithm for jointly estimating the posterior and the noise parameters in
Bayesian inverse problems, which is motivated by indirect measurements and applications …

Learning-based methods have demonstrated remarkable performance in solving inverse
problems, particularly in image reconstruction tasks. Despite their success, these …

In this thesis we investigate two distinct regularizing approaches for solving inverse
problems. The first approach involves assuming that the unknown belongs to a manifold …