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.
Translated title of the contribution | Entropic optimal transport is maximum-likelihood deconvolution |
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Original language | French |
Pages (from-to) | 1228-1235 |
Number of pages | 8 |
Journal | Comptes Rendus Mathematique |
Volume | 356 |
Issue number | 11-12 |
DOIs | |
State | Published - Nov 1 2018 |
ASJC Scopus subject areas
- Mathematics(all)