Quantitative Stability of Barycenters in the Wasserstein Space

09/21/2022
by   Guillaume Carlier, et al.
0

Wasserstein barycenters define averages of probability measures in a geometrically meaningful way. Their use is increasingly popular in applied fields, such as image, geometry or language processing. In these fields however, the probability measures of interest are often not accessible in their entirety and the practitioner may have to deal with statistical or computational approximations instead. In this article, we quantify the effect of such approximations on the corresponding barycenters. We show that Wasserstein barycenters depend in a Hölder-continuous way on their marginals under relatively mild assumptions. Our proof relies on recent estimates that quantify the strong convexity of the dual quadratic optimal transport problem and a new result that allows to control the modulus of continuity of the push-forward operation under a (not necessarily smooth) optimal transport map.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/14/2019

Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space

This work studies an explicit embedding of the set of probability measur...
research
06/26/2015

Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric

Given a family of probability measures in P(X), the space of probability...
research
07/26/2021

Plugin Estimation of Smooth Optimal Transport Maps

We analyze a number of natural estimators for the optimal transport map ...
research
12/26/2019

Learning with Wasserstein barycenters and applications

In this work, learning schemes for measure-valued data are proposed, i.e...
research
10/21/2022

On amortizing convex conjugates for optimal transport

This paper focuses on computing the convex conjugate operation that aris...
research
08/07/2017

Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning

This article introduces a new non-linear dictionary learning method for ...
research
02/27/2023

An Approximation Theory Framework for Measure-Transport Sampling Algorithms

This article presents a general approximation-theoretic framework to ana...

Please sign up or login with your details

Forgot password? Click here to reset