3 papers
math.MG2026
The Observable Wasserstein Distance
Edivaldo Lopes dos Santos, Leandro Vicente Mauri, Washington Mio +1
We introduce the observable Wasserstein distance, a framework for deriving lower bounds on the Wasserstein distance between probability measures on Polish metric spaces, designed t…
math.ST2025
Observable Covariance and Principal Observable Analysis for Data on Metric Spaces
Ece Karacam, Washington Mio, Osman Berat Okutan
Datasets consisting of objects such as shapes, networks, images, or signals overlaid on such geometric objects permeate data science. Such datasets are often equipped with metrics…
math.ST2025
Robust Representation and Estimation of Barycenters and Modes of Probability Measures on Metric Spaces
Washington Mio, Tom Needham
This paper is concerned with the problem of defining and estimating statistics for distributions on spaces such as Riemannian manifolds and more general metric spaces. The challeng…