Pith. sign in

REVIEW 6 cited by

Monte Carlo guided Diffusion for Bayesian linear inverse problems

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2308.07983 v2 pith:IX3KQCMZ submitted 2023-08-15 stat.ML cs.LGstat.ME

Gabriel Cardoso , Yazid Janati El Idrissi , Sylvain Le Corff , Eric Moulines This is my paper · ORCID
classification stat.MLcs.LGstat.ME
keywords inverseproblemsbayesianlinearcarloill-posedmonteposteriors
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference with informative priors to handle the ill-posedness of such problems. Amongst such priors, score-based generative models (SGM) have recently been successfully applied to several different inverse problems. In this study, we exploit the particular structure of the prior defined by the SGM to define a sequence of intermediate linear inverse problems. As the noise level decreases, the posteriors of these inverse problems get closer to the target posterior of the original inverse problem. To sample from this sequence of posteriors, we propose the use of Sequential Monte Carlo (SMC) methods. The proposed algorithm, MCGDiff, is shown to be theoretically grounded and we provide numerical simulations showing that it outperforms competing baselines when dealing with ill-posed inverse problems in a Bayesian setting.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bootstrap Flow-Map Tree Sampling Enables Online Feedback Driven Search

    cs.LG 2026-07 conditional novelty 7.0 of 10

    Bootstrap Flow-Map Trees construct complete DDPM-like trajectories with a single NFE and dynamic steps, enabling efficient online feedback-driven search and alignment that beats prior tree and SMC samplers.

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

    stat.ML 2026-02 conditional novelty 6.0 of 10

    G-DPS is a Gibbs sampler for posterior sampling in linear inverse problems with diffusion priors, using Gaussian conditionals from both the forward and backward diffusion chains.

  3. Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo

    eess.IV 2025-02 conditional novelty 6.0 of 10

    LD-SMC uses sequential Monte Carlo in latent diffusion space with auxiliary per-timestep observations to improve posterior sampling for inverse problems, showing strong gains on inpainting.

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

    cs.LG 2026-07 reject novelty 5.0 of 10

    A SVD-based, coordinate-wise DDIM sampler is claimed to asymptotically sample from the posterior for noisy linear inverse problems, but the proof's posterior identification step does not follow from the stated updates.

  5. CoDe: Blockwise Control for Denoising Diffusion Models

    cs.CV 2025-02 conditional novelty 5.0 of 10

    CoDe applies blockwise best-of-N sampling during diffusion denoising, with Tweedie-based reward estimates, to align generated images to differentiable or non-differentiable rewards.

  6. Local MAP Sampling for Diffusion Models

    cs.GR 2025-10 conditional novelty 4.0 of 10

    LMAPS frames reverse-diffusion inverse-problem solving as repeated local MAP estimation, unifying existing optimization-based solvers, and achieves strong PSNR gains on tasks like motion deblurring, JPEG restoration, ...

Pith tools