5 papers
On quantitative Laplace-type convergence results for some exponential probability measures, with two applications
Valentin De Bortoli, Agnès Desolneux
Laplace-type results characterize the limit of sequence of measures with density w.r.t the Lebesgue measure $(\mathrm{d} Ï_\varepsilon / \mathr…
Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance
Vladimir Petrovic, Rémi Bardenet, Agnès Desolneux
In this paper, we consider the problem of computing the integral of a function on the unit sphere, in any dimension, using Monte Carlo methods. Although the methods we present are…
Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport
Samuel Boïté, Eloi Tanguy, Julie Delon +2
The Expectation-Maximisation (EM) algorithm is a central tool in statistics and machine learning, widely used for latent-variable models such as Gaussian Mixture Models (GMMs). Des…
Constrained Approximate Optimal Transport Maps
Eloi Tanguy, Agnès Desolneux, Julie Delon
We investigate finding a map within a function class that minimises an Optimal Transport (OT) cost between a target measure and the image by of a source measure $Î…
Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space
Antoine Salmona, Julie Delon, Agnès Desolneux
The Gromov-Wasserstein (GW) distance is frequently used in machine learning to compare distributions across distinct metric spaces. Despite its utility, it remains computationally…