25 citations · 55 across the 5 of their papers we have counts for
6 papers
Reweighted information inequalities
Jonathan Niles-Weed
We establish a variant of the log-Sobolev and transport-information inequalities for mixture distributions. If a probability measure can be decomposed into components that indi…
Weak limits of entropy regularized Optimal Transport; potentials, plans and divergences
Alberto Gonzalez-Sanz, Jean-Michel Loubes, Jonathan Niles-Weed
This work deals with the asymptotic distribution of both potentials and couplings of entropic regularized optimal transport for compactly supported probabilities in . We firs…
Asymptotics of smoothed Wasserstein distances in the small noise regime
Yunzi Ding, Jonathan Niles-Weed
We study the behavior of the Wasserstein- distance between discrete measures and in when both measures are smoothed by small amounts of Gaussian noise. Th…
An improved central limit theorem and fast convergence rates for entropic transportation costs
Eustasio del Barrio, Alberto Gonzalez-Sanz, Jean-Michel Loubes +1
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 allo…
Asymptotics for semi-discrete entropic optimal transport
Jason M. Altschuler, Jonathan Niles-Weed, Austin J. Stromme
We compute exact second-order asymptotics for the cost of an optimal solution to the entropic optimal transport problem in the continuous-to-discrete, or semi-discrete, setting. In…
Massively scalable Sinkhorn distances via the Nyström method
Jason Altschuler, Francis Bach, Alessandro Rudi +1
The Sinkhorn "distance", a variant of the Wasserstein distance with entropic regularization, is an increasingly popular tool in machine learning and statistical inference. However,…