English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We prove several fundamental statistical bounds for entropic OT with thesquared Euclidean cost between subgaussian probability measures in arbitrarydimension. First, through a new sample complexity result we establish the rateof convergence of entropic OT for empirical measures. Our analysis improvesexponentially on the bound of Genevay et al. (2019) and extends their work tounbounded measures. Second, we establish a central limit theorem for entropicOT, based on techniques developed by Del Barrio and Loubes (2019). Previously,such a result was only known for finite metric spaces. As an application of ourresults, we develop and analyze a new technique for estimating the entropy of arandom variable corrupted by gaussian noise.

Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem