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Cesar Uribe

2 accepted papers

2019

On the Complexity of Approximating Wasserstein Barycenters

ICML 2019oral

We study the complexity of approximating the Wasserstein barycenter of $m$ discrete measures, or histograms of size $n$, by contrasting two alternative approaches that use entropic regularization. The first approach is based on the Iterative Bregman Projections (IBP) algorithm for which our novel an…

Cited by 124SourcePDFScholar
2018

Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters

NeurIPS 2018spotlight

We study the decentralized distributed computation of discrete approximations for the regularized Wasserstein barycenter of a finite set of continuous probability measures distributedly stored over a network. We assume there is a network of agents/machines/computers, and each agent holds a private c…

Cited by 129SourcePDFScholar