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Poster
in
Workshop: Optimal Transport and Machine Learning

A Central Limit Theorems for Multidimensional Wasserstein Distances

Alberto Gonzalez Sanz · Loubes Jean-Michel · Eustasio Barrio


Abstract: We present recent approaches to prove the asymptotic behaviour of empirical transport cost, Tc(Pn,Q), under minimal assumptions in high dimension. Centering around its expectation, the weak limit of n{Tc(Pn,Q)ETc(Pn,Q)} is Gaussian. Yet, due to the curse of dimensionality, the variable ETc(Pn,Q) can not be exchanged by its population counterpart Tc(P,Q). When P is finitely supported this problem can be solved and the limit becomes the supremum of a centered Gaussian process, which is Gaussian under some additional conditions on the probability Q.

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