Most graph kernels are an instance of the class of R-Convolution kernels, which measure
the similarity of objects by comparing their substructures. Despite their empirical success …

In this paper, we introduce a notion of barycenter in the Wasserstein space which
generalizes McCann's interpolation to the case of more than two measures. We provide …

Given a distribution ρ ρ on persistence diagrams and observations X_ 1, ..., X_ n ∼\limits^
iid ρ X 1,…, X n∼ iid ρ we introduce an algorithm in this paper that estimates a Fréchet …

Based on the Fréchet mean, we define a notion of barycenter corresponding to a usual
notion of statistical mean. We prove the existence of Wasserstein barycenters of random …

This work establishes fast rates of convergence for empirical barycenters over a large class
of geodesic spaces with curvature bounds in the sense of Alexandrov. More specifically, we …

This paper provides rates of convergence for empirical (generalised) barycenters on
compact geodesic metric spaces under general conditions using empirical processes …

This study deals with the problem of covariance matrix estimation for radar signal processing
applications. The authors propose and analyse a class of estimators that do not require any …

The reliability of a phylogenetic inference method from genomic sequence data is ensured
by its statistical consistency. Bayesian inference methods produce a sample of phylogenetic …

We formulate and study an optimal transportation problem with infinitely many marginals;
this is a natural extension of the multi-marginal problem studied by Gangbo and Świȩch …

The authors consider the problem of covariance matrix estimation in heterogeneous
environments for radar signal processing applications, where the secondary data exhibit …