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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 93Source ↗PDF ↗Scholar ↗
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 129Source ↗PDF ↗Scholar ↗