arXiv Machine Learning July 8, 2026 By Jean-Fran\c{c}ois Giovannelli

A Gibbs posterior sampler for inverse problem based on prior diffusion model

Read the original on arXiv Machine Learning →

arXiv:2602. 11059v2 Announce Type: replace-cross Abstract: This paper addresses the issue of inversion in cases where (1) the observation system is modeled by a linear transformation and additive error, (2) the problem is ill-posed and regularization relies on a Bayesian strategy, (3)~the prior is modeled by a diffusion process adjusted on an available large set of examples.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

diffusion

arXiv Machine Learning

Jul 8

Estimation of instrument and noise parameters for inverse problem based on prior diffusion model

arXiv:2602. 11711v2 Announce Type: replace-cross Abstract: This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems.

By Jean-Fran\c{c}ois Giovannelli

diffusion

arXiv AI

Jul 22

Provable diffusion-based posterior sampling for linear inverse problems via DDIM

arXiv:2607. 19333v1 Announce Type: cross Abstract: Diffusion-based methods have achieved remarkable empirical success in solving inverse problems.

By Yuchen Jiao, Na Li, Changxiao Cai, Yuxin Chen, Gen Li

diffusion

arXiv Machine Learning

Aug 11

An invertible generative model for forward and inverse problems

arXiv:2509. 03910v2 Announce Type: replace-cross Abstract: We formulate inverse problems in a Bayesian framework and aim to train an invertible generative model that is capable of simulation (i.

By Christoph Brune, Marcello Carioni, Tristan van Leeuwen, Lasse Veenstra

arXiv Machine Learning

Jul 28

Exact Evaluation of the Accuracy of Diffusion Models for Inverse Problems with Gaussian Data Distributions

arXiv:2507. 07008v2 Announce Type: replace Abstract: Used as priors for Bayesian inverse problems, diffusion models have recently attracted considerable attention in the literature.

By Emile Pierret, Bruno Galerne

diffusion

arXiv Machine Learning

Aug 3

Provable Diffusion Posterior Sampling for Bayesian Inversion

arXiv:2512. 08022v2 Announce Type: replace-cross Abstract: We propose a novel diffusion-based posterior sampling method within a plug-and-play framework.

By Jinyuan Chang, Chenguang Duan, Yuling Jiao, Ruoxuan Li, Jerry Zhijian Yang, Cheng Yuan

diffusion multimodal

arXiv Machine Learning

Jun 2

Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces

arXiv:2603. 14798v2 Announce Type: replace-cross Abstract: We propose a machine-learning algorithm for Bayesian inverse problems in the function-space regime.

By Zilan Cheng, Li-Lian Wang, Zhongjian Wang

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