25 citations · 27 across the 11 of their papers we have counts for
11 papers
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…
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…
Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
Maxence Noble, Gonzalo Iñaki Quintana, Benjamin Aubin +1
Diffusion-based image super-resolution (SR) has recently attracted significant attention by leveraging the expressive power of large pre-trained text-to-image diffusion models (DMs…
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…
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…
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…
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…
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
Maxence Noble, Valentin De Bortoli, Arnaud Doucet +1
Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at minimizing the integral of a cost function with respect to a distribution with some prescribed marginals. In…
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach
Giacomo Greco, Maxence Noble, Giovanni Conforti +1
Computational optimal transport (OT) has recently emerged as a powerful framework with applications in various fields. In this paper we focus on a relaxation of the original OT pro…
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
Maxence Noble, Valentin De Bortoli, Alain Durmus
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a version of the HMC algorithm which aims at sampling from a Gibbs distribution on a manifold , en…
Differentially Private Federated Learning on Heterogeneous Data
Maxence Noble, Aurélien Bellet, Aymeric Dieuleveut
Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two key challenges: (i) efficient training from highly heterogeneous user data, and (ii) prot…