2 citations · 5 across the 9 of their papers we have counts for
9 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…
Robust Barycenters of Persistence Diagrams
Keanu Sisouk, Eloi Tanguy, Julie Delon +1
This short paper presents a general approach for computing robust Wasserstein barycenters of persistence diagrams. The classical method consists in computing assignment arithmetic…
Sliced Transport Plans
Eloi Tanguy, Laetitia Chapel, Julie Delon
Since the introduction of the Sliced Wasserstein distance in the literature, its simplicity and efficiency have made it one of the most interesting surrogate for the Wasserstein di…
Explicit Universal and Approximate-Universal Kernels on Compact Metric Spaces
Eloi Tanguy
Universal kernels, whose Reproducing Kernel Hilbert Space is dense in the space of continuous functions are of great practical and theoretical interest. In this paper, we introduce…
Computing Barycentres of Measures for Generic Transport Costs
Eloi Tanguy, Julie Delon, Nathaël Gozlan
Wasserstein barycentres represent average distributions between multiple probability measures for the Wasserstein distance. The numerical computation of Wasserstein barycentres is…
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 …
Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
Eloi Tanguy
Optimal Transport has sparked vivid interest in recent years, in particular thanks to the Wasserstein distance, which provides a geometrically sensible and intuitive way of compari…
Properties of Discrete Sliced Wasserstein Losses
Eloi Tanguy, Rémi Flamary, Julie Delon
The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processi…
Reconstructing discrete measures from projections. Consequences on the empirical Sliced Wasserstein Distance
Eloi Tanguy, Rémi Flamary, Julie Delon
This paper deals with the reconstruction of a discrete measure on from the knowledge of its pushforward measures by linear applications $P_i: \mathb…