19 citations · 48 across the 18 of their papers we have counts for
20 papers
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…
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…
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 …
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…
Can Push-forward Generative Models Fit Multimodal Distributions?
Antoine Salmona, Valentin de Bortoli, Julie Delon +1
Many generative models synthesize data by transforming a standard Gaussian random variable using a deterministic neural network. Among these models are the Variational Autoencoders…
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} π_\varepsilon / \mathrm{…