Conditional Generative Models are Provably Robust: Pointwise Guarantees
for Bayesian Inverse Problems

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems

28 March 2023

Fabian Altekrüger

Gabriele Steidl

Papers citing "Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems"

12 / 12 papers shown

Title
A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models Shivam Kumar Yun Yang Lizhen Lin 18 0 0 02 Oct 2024
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching Jannis Chemseddine Paul Hagemann Gabriele Steidl Christian Wald 38 9 0 27 Mar 2024
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview Alexander Auras Kanchana Vaishnavi Gandikota Hannah Droege Michael Moeller AAML 21 0 0 19 Feb 2024
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction Moritz Piening Fabian Altekrüger J. Hertrich Paul Hagemann Andrea Walther Gabriele Steidl 16 5 0 27 Dec 2023
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances Jannis Chemseddine Paul Hagemann Christian Wald 19 3 0 20 Oct 2023
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel Paul Hagemann J. Hertrich Fabian Altekrüger Robert Beinert Jannis Chemseddine Gabriele Steidl 8 23 0 04 Oct 2023
Adversarial robustness of amortized Bayesian inference Manuel Glöckler Michael Deistler Jakob H. Macke AAML 9 13 0 24 May 2023
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems Ziruo Cai Junqi Tang Subhadip Mukherjee Jinglai Li Carola Bibiane Schönlieb Xiaoqun Zhang AI4CE 17 3 0 17 Apr 2023
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics A. Garbuno-Iñigo T. Helin Franca Hoffmann Bamdad Hosseini 23 9 0 02 Mar 2023
On Adversarial Robustness of Deep Image Deblurring Kanchana Vaishnavi Gandikota Paramanand Chandramouli Michael Moeller 31 11 0 05 Oct 2022
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence A. Andrle N. Farchmin Paul Hagemann Sebastian Heidenreich V. Soltwisch Gabriele Steidl 52 15 0 05 Feb 2021
Well-posed Bayesian Inverse Problems with Infinitely-Divisible and Heavy-Tailed Prior Measures Bamdad Hosseini 32 34 0 23 Sep 2016