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

*Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics*, PMLR 108:3186-3196, 2020.

#### Abstract

This work studies an explicit embedding of the set of probability measures into a Hilbert space, defined using optimal transport maps from a reference probability density. This embedding linearizes to some extent the 2-Wasserstein space and is shown to be bi-Hölder continuous. It enables the direct use of generic supervised and unsupervised learning algorithms on measure data consistently w.r.t. the Wasserstein geometry.