6 papers · 1 filter
Twisted Schrödinger Bridge Matching
Maxence Noble, Marie Scheid, Yazid Janati +2
Over the past few years, diffusion-based Schrödinger bridge models have been proposed to approximate optimal transport dynamics between two prescribed boundary distributions, with…
Stochastic Localization via Iterative Posterior Sampling
Louis Grenioux, Maxence Noble, Marylou Gabrié +1
Building upon score-based learning, new interest in stochastic localization techniques has recently emerged. In these models, one seeks to noise a sample from the data distribution…
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…
Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
Louis Grenioux, Maxence Noble
Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics. Boltzmann Generators (BGs) tackle it by combining a generative model with a Mont…
Learned Reference-based Diffusion Sampling for multi-modal distributions
Maxence Noble, Louis Grenioux, Marylou Gabrié +1
Over the past few years, several approaches utilizing score-based diffusion have been proposed to sample from probability distributions, that is without having access to exact samp…
Improving the evaluation of samplers on multi-modal targets
Louis Grenioux, Maxence Noble, Marylou Gabrié
Addressing multi-modality constitutes one of the major challenges of sampling. In this reflection paper, we advocate for a more systematic evaluation of samplers towards two source…