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stat.ML2026
Mixed Poisson diffusion models and application to posterior sampling
Thibaut Pellerin, Alain Durmus, Elinor Berger +1
Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely on Gaussian noising processes,…
stat.ML2026
Sampling from multi-modal distributions on Riemannian manifolds with training-free stochastic interpolants
Alain Durmus, Maxence Noble, Thibaut Pellerin
In this paper, we propose a general methodology for sampling from un-normalized densities defined on Riemannian manifolds, with a particular focus on multi-modal targets that remai…