Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters

NeurIPS 2018 Pavel DvurechenskiiDarina DvinskikhAlexander GasnikovCesar UribeAngelia Nedich

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 continuous probability measure and seeks to compute the barycenter of all the measures in the network by getting samples from its local measure and exchanging information with its neighbors... (read more)

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