RT info:eu-repo/semantics/article
T1 An Improved Central Limit Theorem and Fast Convergence Rates for Entropic Transportation Costs
A1 del Barrio, Eustasio
A1 Sanz, Alberto González
A1 Loubes, Jean-Michel
A1 Niles-Weed, Jonathan
K1 Estadística
K1 Probabilidad
K1 optimal transport, entropic regularization, central limit theorem, Sinkhorn divergence
AB We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost. This is the first result which allows for asymptotically valid inference for entropic optimal transport between measures which are not necessarily discrete. In the compactly supported case, we complement these results with new, faster, convergence rates for the expected entropic transportation cost between empirical measures. Our proof is based on strengthening convergence results for dual solutions to the entropic optimal transport problem.
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/82399
UL https://uvadoc.uva.es/handle/10324/82399
LA spa
NO SIAM Journal on Mathematics of Data Science, Vol. 5, Iss. 3 (2023), 639-669.
NO Producción Científica
DS UVaDOC
RD 30-ene-2026