Pith. sign in

REVIEW 1 cited by

Limit Theorems for Entropic Optimal Transport Maps and the Sinkhorn Divergence

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2207.08683 v2 pith:PHIR5XV2 submitted 2022-07-18 math.ST math.PRstat.TH

classification math.STmath.PRstat.TH
keywords limitsinkhorndivergencemapspotentialstheoremsdifferentiabilityempirical
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We study limit theorems for entropic optimal transport (EOT) maps, dual potentials, and the Sinkhorn divergence. The key technical tool we use is a first and second-order Hadamard differentiability analysis of EOT potentials with respect to the marginal distributions, which may be of independent interest. Given the differentiability results, the functional delta method is used to obtain central limit theorems for empirical EOT potentials and maps. The second-order functional delta method is leveraged to establish the limit distribution of the empirical Sinkhorn divergence under the null. Building on the latter result, we further derive the null limit distribution of the Sinkhorn independence test statistic and characterize the correct order. Since our limit theorems follow from Hadamard differentiability of the relevant maps, as a byproduct, we also obtain bootstrap consistency and asymptotic efficiency of the empirical EOT map, potentials, and Sinkhorn divergence.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Coreset selection for the Sinkhorn divergence and generic smooth divergences

    stat.ML 2025-04 conditional novelty 8.0 of 10

    CO2 reduces coreset selection for any smooth divergence to MMD minimization and proves that Sinkhorn divergence coresets of size m=ω(log^d n) match the error of the full empirical measure.

Pith tools