Defines Hausdorff-style and Wasserstein-style metrics on C-sets, proving the latter are convex relaxations of the former and computable as linear programs.
Entropic optimal transport is maximum-likelihood deconvolution
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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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math.OC 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Hausdorff and Wasserstein metrics on graphs and other structured data
Defines Hausdorff-style and Wasserstein-style metrics on C-sets, proving the latter are convex relaxations of the former and computable as linear programs.