Computing optimal transport distances such as the earth mover's distance is a fundamental
problem in machine learning, statistics, and computer vision. Despite the recent introduction …

Wasserstein discriminant analysis (WDA) is a new supervised linear dimensionality
reduction algorithm. Following the blueprint of classical Fisher Discriminant Analysis, WDA …

Abstract Existing Source-Free Domain Adaptation (SFDA) methods typically adopt the
feature distribution alignment paradigm via mining auxiliary information (eg., pseudo …

Two semimetrics on probability distributions are proposed, given as the sum of differences of
expectations of analytic functions evaluated at spatial or frequency locations (ie, features) …

Cette note donne un interprétation statistique du transport optimal entropique: on montre
que l'estimateur du maximum de vraisemblance en deconvolution gaussienne correspond à …

We are interested in the computation of the transport map of an Optimal Transport problem.
Most of the computational approaches of Optimal Transport use the Kantorovich relaxation …

Estimating dynamic functional network connectivity (dFNC) of the brain from functional
magnetic resonance imaging (fMRI) data can reveal both spatial and temporal organization …

We analyze a quantum version of the Monge–Kantorovich optimal transport problem. The
quantum transport cost related to a Hermitian cost matrix C is minimized over the set of all …

We study streaming algorithms for two fundamental geometric problems: computing the cost
of a Minimum Spanning Tree (MST) of an n-point set X⊂{1, 2,…, Δ} d, and computing the …

We develop a projected Wasserstein distance for the two-sample test, a fundamental
problem in statistics and machine learning: given two sets of samples, to determine whether …