1 citations · 1 across the 10 of their papers we have counts for
11 papers
Beckmann Transport Models: From Autonomous Flows to One-Step Maps
Lee Cheuk-Kit, Florentin Coeurdoux, Yuyuan Chen +5
We propose an instantiation of flow matching that relies on a time-independent velocity field (an \emph{autonomous flow}) to exactly map between two distributions, so long as the t…
Covariance Shrinkage via Stochastic Interpolation
Mathieu Chalvidal, Florentin Coeurdoux, Eric Vanden-Eijnden
We recast classical shrinkage of high-dimensional covariance estimators as empirical risk minimization over a parametric stochastic interpolant between a source and a target distri…
Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario
Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe Bouchaud
Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide…
Probing the Geometry of Diffusion Models with the String Method
Elio Moreau, Florentin Coeurdoux, Grégoire Ferre +1
Understanding the geometry of learned distributions is fundamental to improving and interpreting diffusion models, yet systematic tools for exploring their landscape remain limited…
Generative Modeling via Kernelized Stochastic Interpolants
Florentin Coeurdoux, Etienne Lempereur, Nathanaël Cuvelle-Magar +2
We develop a kernel method for generative modeling within the stochastic interpolant framework, replacing neural network training with linear systems. The drift of the generative S…
MGD: Moment Guided Diffusion for Maximum Entropy Generation
Etienne Lempereur, Nathanaël Cuvelle--Magar, Florentin Coeurdoux +2
Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification from…
Multitask Learning with Stochastic Interpolants
Hugo Negrel, Florentin Coeurdoux, Michael S. Albergo +1
We propose a framework for learning maps between probability distributions that broadly generalizes the time dynamics of flow and diffusion models. To enable this, we generalize st…
Normalizing flow sampling with Langevin dynamics in the latent space
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Normalizing flows (NF) use a continuous generator to map a simple latent (e.g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.…
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based…
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e.g., images) and their failing to det…
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Optimal transport (OT) provides effective tools for comparing and mapping probability measures. We propose to leverage the flexibility of neural networks to learn an approximate op…