Minimal Lipschitz Extensions for Vector-Valued Functions on Finite Graphs. - Johannes Hertrich; Miroslav Bacák; Sebastian Neumayer; Gabriele Steidl (SSVM, 2019)

Alternatives of the EM Algorithm for Estimating the Parameters of the Student-t Distribution. - Marzieh Hasannasab; Johannes Hertrich; Friederike Laus; Gabriele Steidl (CoRR, 2019)

Infinity-Laplacians on Scalar- and Vector-Valued Functions and Optimal Lipschitz Extensions on Graphs. - Johannes Hertrich (CoRR, 2019)

Parseval Proximal Neural Networks. - Marzieh Hasannasab; Johannes Hertrich; Sebastian Neumayer; Gerlind Plonka; Simon Setzer; Gabriele Steidl (CoRR, 2019)

Inertial Stochastic PALM and its Application for Learning Student-t Mixture Models. - Johannes Hertrich; Gabriele Steidl (CoRR, 2020)

PCA Reduced Gaussian Mixture Models with Applications in Superresolution. - Johannes Hertrich; Dang Phoung Lan Nguyen; Jean-François Aujol; Dominique Bernard; Yannick Berthoumieu; Abdellativ Sa'adaldin; Gabriele Steidl (CoRR, 2020)

Convolutional Proximal Neural Networks and Plug-and-Play Algorithms. - Johannes Hertrich; Sebastian Neumayer; Gabriele Steidl (CoRR, 2020)

Variational Models for Color Image Correction Inspired by Visual Perception and Neuroscience. - Thomas Batard; Johannes Hertrich; Gabriele Steidl (J. Math. Imaging Vis., 2020)

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

Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution. - Marzieh Hasannasab; Johannes Hertrich; Friederike Laus; Gabriele Steidl (Numer. Algorithms, 2021)

Correction to: Alternatives to the EM algorithm for ML estimation of location, scatter matrix, and degree of freedom of the Student t distribution. - Marzieh Hasannasab; Johannes Hertrich; Friederike Laus; Gabriele Steidl (Numer. Algorithms, 2021)

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution. - Fabian Altekrüger; Johannes Hertrich (CoRR, 2022)

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

Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels. - Johannes Hertrich; Manuel Gräf; Robert Beinert; Gabriele Steidl (CoRR, 2022)

Wasserstein Patch Prior for Image Superresolution. - Johannes Hertrich; Antoine Houdard; Claudia Redenbach (IEEE Trans. Computational Imaging, 2022)

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

Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels. - Fabian Altekrüger; Johannes Hertrich; Gabriele Steidl (ICML, 2023)

Proximal Residual Flows for Bayesian Inverse Problems. - Johannes Hertrich (SSVM, 2023)

Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line. - Johannes Hertrich; Robert Beinert; Manuel Gräf; Gabriele Steidl (SSVM, 2023)

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels. - Fabian Altekrüger; Johannes Hertrich; Gabriele Steidl (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)

Proximal neural networks and stochastic normalizing flows for inverse problems. - Johannes Hertrich (2023)

WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution. - Fabian Altekrüger; Johannes Hertrich (SIAM J. Imaging Sci., 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)

Importance Corrected Neural JKO Sampling. - Johannes Hertrich; Robert Gruhlke (CoRR, 2024)

Generative Feature Training of Thin 2-Layer Networks. - Johannes Hertrich; Sebastian Neumayer (CoRR, 2024)

Manifold Learning by Mixture Models of VAEs for Inverse Problems. - Giovanni S. Alberti; Johannes Hertrich; Matteo Santacesaria; Silvia Sciutto (J. Mach. Learn. Res., 2024)

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

Fast Kernel Summation in High Dimensions via Slicing and Fourier Transforms. - Johannes Hertrich (SIAM J. Math. Data Sci., 2024)

Fast Summation of Radial Kernels via QMC Slicing. - Johannes Hertrich; Tim Jahn; Michael Quellmalz (ICLR, 2025)

Plug-and-Play Half-Quadratic Splitting for Ptychography. - Alexander Denker; Johannes Hertrich; Zeljko Kereta; Silvia Cipiccia; Ecem Erin; Simon R. Arridge (SSVM (1), 2025)

Iterative Importance Fine-tuning of Diffusion Models. - Alexander Denker; Shreyas Padhy; Francisco Vargas 001; Johannes Hertrich (CoRR, 2025)

On the Relation between Rectified Flows and Optimal Transport. - Johannes Hertrich; Antonin Chambolle; Julie Delon (CoRR, 2025)