Stabilizing Invertible Neural Networks Using Mixture Models. - Paul Hagemann; Sebastian Neumayer (CoRR, 2020)

Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence. - Anna Andrle; Nando Farchmin; Paul Hagemann; Sebastian Heidenreich; Victor Soltwisch; Gabriele Steidl (SSVM, 2021)

A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains. - Johannes Hertrich; Paul Hagemann; Gabriele Steidl (CoRR, 2021)

PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization. - Fabian Altekrüger; Alexander Denker; Paul Hagemann; Johannes Hertrich; Peter Maass; Gabriele Steidl (CoRR, 2022)

Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint. - Paul Hagemann; Johannes Hertrich; Gabriele Steidl (SIAM/ASA J. Uncertain. Quantification, 2022)

Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation. - Paul Hagemann; Lars Ruthotto; Gabriele Steidl; Nicole Tianjiao Yang (CoRR, 2023)

Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances. - Jannis Chemseddine; Paul Hagemann; Christian Wald (CoRR, 2023)

Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction. - Moritz Piening; Fabian Altekrüger; Johannes Hertrich; Paul Hagemann; Andrea Walther; Gabriele Steidl (CoRR, 2023)

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems. - Fabian Altekrüger; Paul Hagemann; Gabriele Steidl (Trans. Mach. Learn. Res., 2023)

Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel. - Paul Hagemann; Johannes Hertrich; Fabian Altekrüger; Robert Beinert; Jannis Chemseddine; Gabriele Steidl (ICLR, 2024)

Generative Sliced MMD Flows with Riesz Kernels. - Johannes Hertrich; Christian Wald; Fabian Altekrüger; Paul Hagemann (ICLR, 2024)

Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching. - Jannis Chemseddine; Paul Hagemann; Christian Wald; Gabriele Steidl (CoRR, 2024)

Mixed noise and posterior estimation with conditional deepGEM. - Paul Hagemann; Johannes Hertrich; Maren Casfor; Sebastian Heidenreich; Gabriele Steidl (Mach. Learn. Sci. Technol., 2024)

PnP-Flow: Plug-and-Play Image Restoration with Flow Matching. - Ségolène Tiffany Martin; Anne Gagneux; Paul Hagemann; Gabriele Steidl (ICLR, 2025)

Sampling from Boltzmann Densities with Physics Informed Low-Rank Formats. - Paul Hagemann; Janina Schütte; David Sommer; Martin Eigel; Gabriele Steidl (SSVM (1), 2025)

Trajectory Generator Matching for Time Series. - T. Jahn; Jannis Chemseddine; Paul Hagemann; Christian Wald; Gabriele Steidl (CoRR, 2025)