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, , under minimal assumptions in high dimension. Centering around its expectation, the weak limit of is Gaussian. Yet, due to the curse of dimensionality, the variable can not be exchanged by its population counterpart . When 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 .
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