activity
20032025
most citedTransportation cost-information inequalities and applications to random dynamical systems and diffusions

255 citations · 949 across the 71 of their papers we have counts for

collaborators

82 papers

math.PR2025

Diffusion annealed Langevin dynamics: a theoretical study

Patrick Cattiaux, Paula Cordero-Encinar, Arnaud Guillin

In this work we study the diffusion annealed Langevin dynamics, a score-based diffusion process recently introduced in the theory of generative models and which is an alternative t…

math.PR2023

Some remarks on the effect of the Random Batch Method on phase transition

Arnaud Guillin, Pierre Le Bris, Pierre Monmarché

In this article, we focus on two toy models : the Curie-Weiss model and the system of particles in linear interactions in a double well confining potential. Both models, which…

stat.ML2023

Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference

Arnaud Descours, Tom Huix, Arnaud Guillin +3

We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case. We consider a regression problem with…

cond-mat.stat-mech2023

Necessary and sufficient symmetries in Event-Chain Monte Carlo with generalized flows and Application to hard dimers

Tristan Guyon, Arnaud Guillin, Manon Michel

Event-Chain Monte Carlo (ECMC) methods generate continuous-time and non-reversible Markov processes which often display significant accelerations compared to reversible counterpart…

cond-mat.stat-mech2023

Jamming pair of general run-and-tumble particles: Exact results, symmetries and steady-state universality classes

Leo Hahn, Arnaud Guillin, Manon Michel

While run-and-tumble particles are a foundational model for self-propelled particles as bacteria or Janus particles, the analytical derivation of their steady state from the micros…

cs.LG2023

Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity

Vincent Souveton, Arnaud Guillin, Jens Jasche +2

Normalizing Flows (NF) are Generative models which transform a simple prior distribution into the desired target. They however require the design of an invertible mapping whose Jac…