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

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 enables the direct use of generic supervised and unsupervised learning algorithms on measure data. Our main result is that the embedding is (bi-)Holder continuous, when the reference density is uniform over a convex set, and can be equivalently phrased as a dimension-independent Holder-stability results for optimal transport maps.