1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…