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Entropic optimal transport is maximum-likelihood deconvolution
Published 14 Sep 2018 in math.ST and stat.TH | (1809.05572v2)
Abstract: We give a statistical interpretation of entropic optimal transport by showing that performing maximum-likelihood estimation for Gaussian deconvolution corresponds to calculating a projection with respect to the entropic optimal transport distance. This structural result gives theoretical support for the wide adoption of these tools in the machine learning community.
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