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stat.ML2025
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…
stat.ML2024
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…
stat.ML2024
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…