Paul Hagemann

28 papers A* 3Journal 23Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching.
J. Mach. Learn. Res.
Jannis Chemseddine, Paul Hagemann, Gabriele Steidl, Christian Wald
2025 J jnl
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation.
SIAM J. Math. Data Sci.
Paul Hagemann, Sophie Mildenberger, Lars Ruthotto, Gabriele Steidl, Nicole Tianjiao Yang
2025 A* conf
PnP-Flow: Plug-and-Play Image Restoration with Flow Matching.
ICLR
Ségolène Tiffany Martin, Anne Gagneux, Paul Hagemann, Gabriele Steidl
2025 J jnl
Provable Mixed-Noise Learning with Flow-Matching.
CoRR
Paul Hagemann, Robert Gruhlke, Bernhard Stankewitz, Claudia Schillings, Gabriele Steidl
2025 — conf
Sampling from Boltzmann Densities with Physics Informed Low-Rank Formats.
SSVM (1)
Paul Hagemann, Janina Schütte, David Sommer, Martin Eigel, Gabriele Steidl
2025 J jnl
Trajectory Generator Matching for Time Series.
CoRR
T. Jahn, Jannis Chemseddine, Paul Hagemann, Christian Wald, Gabriele Steidl
2025 J jnl
Transport Novelty Distance: A Distributional Metric for Evaluating Material Generative Models.
CoRR
Paul Hagemann, Simon Müller, Janine George, Philipp Benner
2024 J jnl
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching.
CoRR
Jannis Chemseddine, Paul Hagemann, Christian Wald, Gabriele Steidl
2024 A* conf
Generative Sliced MMD Flows with Riesz Kernels.
ICLR
Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann
2024 J jnl
Mixed Noise and Posterior Estimation with Conditional DeepGEM.
CoRR
Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl
2024 J jnl
Mixed noise and posterior estimation with conditional deepGEM.
Mach. Learn. Sci. Technol.
Paul Hagemann, Johannes Hertrich, Maren Casfor, Sebastian Heidenreich, Gabriele Steidl
2024 J jnl
PnP-Flow: Plug-and-Play Image Restoration with Flow Matching.
CoRR
Ségolène Martin, Anne Gagneux, Paul Hagemann, Gabriele Steidl
2024 A* conf
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel.
ICLR
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert, Jannis Chemseddine, Gabriele Steidl
2024 J jnl
Sampling from Boltzmann densities with physics informed low-rank formats.
CoRR
Paul Hagemann, Janina Schütte, David Sommer, Martin Eigel, Gabriele Steidl
2023 J jnl
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems.
CoRR
Fabian Altekrüger, Paul Hagemann, Gabriele Steidl
2023 J jnl
Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems.
Trans. Mach. Learn. Res.
Fabian Altekrüger, Paul Hagemann, Gabriele Steidl
2023 J jnl
Generative Sliced MMD Flows with Riesz Kernels.
CoRR
Johannes Hertrich, Christian Wald, Fabian Altekrüger, Paul Hagemann
2023 J jnl
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction.
CoRR
Moritz Piening, Fabian Altekrüger, Johannes Hertrich, Paul Hagemann, Andrea Walther, Gabriele Steidl
2023 J jnl
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation.
CoRR
Paul Hagemann, Lars Ruthotto, Gabriele Steidl, Nicole Tianjiao Yang
2023 J jnl
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel.
CoRR
Paul Hagemann, Johannes Hertrich, Fabian Altekrüger, Robert Beinert, Jannis Chemseddine, Gabriele Steidl
2023 J jnl
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances.
CoRR
Jannis Chemseddine, Paul Hagemann, Christian Wald
2022 J jnl
PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization.
CoRR
Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl
2022 J jnl
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint.
SIAM/ASA J. Uncertain. Quantification
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
2021 J jnl
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains.
CoRR
Johannes Hertrich, Paul Hagemann, Gabriele Steidl
2021 — conf
Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence.
SSVM
Anna Andrle, Nando Farchmin, Paul Hagemann, Sebastian Heidenreich, Victor Soltwisch, Gabriele Steidl
2021 J jnl
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence.
CoRR
Anna Andrle, Nando Farchmin, Paul Hagemann, Sebastian Heidenreich, Victor Soltwisch, Gabriele Steidl
2021 J jnl
Stochastic Normalizing Flows for Inverse Problems: a Markov Chains Viewpoint.
CoRR
Paul Hagemann, Johannes Hertrich, Gabriele Steidl
2020 J jnl
Stabilizing Invertible Neural Networks Using Mixture Models.
CoRR
Paul Hagemann, Sebastian Neumayer