Horizontally Scalable Submodular Maximization

31 May 2016Mario LucicOlivier BachemMorteza ZadimoghaddamAndreas Krause

A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization. Existing approaches for distributed submodular maximization have a critical drawback: The capacity - number of instances that can fit in memory - must grow with the data set size... (read more)

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