3 citations · 11 across the 18 of their papers we have counts for
6 papers · 1 filter
Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference
Léon Zheng, Thomas Hirtz, Yazid Janati +1
Zero-shot diffusion posterior sampling offers a flexible framework for inverse problems by accommodating arbitrary degradation operators at test time, but incurs high computational…
Iterative Tilting for Diffusion Fine-Tuning
Jean Pachebat, Giovanni Conforti, Alain Durmus +1
We introduce iterative tilting, a gradient-free method for fine-tuning diffusion models toward reward-tilted distributions. The method decomposes a large reward tilt int…
A Mixture-Based Framework for Guiding Diffusion Models
Yazid Janati, Badr Moufad, Mehdi Abou El Qassime +3
Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide…
Variational Diffusion Posterior Sampling with Midpoint Guidance
Badr Moufad, Yazid Janati, Lisa Bedin +4
Diffusion models have recently shown considerable potential in solving Bayesian inverse problems when used as priors. However, sampling from the resulting denoising posterior distr…
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
Yazid Janati, Badr Moufad, Alain Durmus +2
Recent advancements in solving Bayesian inverse problems have spotlighted denoising diffusion models (DDMs) as effective priors. Although these have great potential, DDM priors yie…
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff +1
Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference…