Entropic optimal transport is maximum-likelihood deconvolution

Philippe Rigollet; Jonathan Weed

doi:10.1016/j.crma.2018.10.010

Entropic optimal transport is maximum-likelihood deconvolution

Philippe Rigollet, Jonathan Weed

Mathematics

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	1228-1235
Number of pages	8
Journal	Comptes Rendus Mathematique
Volume	356
Issue number	11-12
DOIs	https://doi.org/10.1016/j.crma.2018.10.010
State	Published - Nov 1 2018

ASJC Scopus subject areas

General Mathematics

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10.1016/j.crma.2018.10.010

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title = "Entropic optimal transport is maximum-likelihood deconvolution",

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.",

author = "Philippe Rigollet and Jonathan Weed",

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Entropic optimal transport is maximum-likelihood deconvolution

Abstract

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