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Distributed Submodular Maximization
[article]
2016
arXiv
pre-print
Many large-scale machine learning problems--clustering, non-parametric learning, kernel machines, etc.--require selecting a small yet representative subset from a large dataset. Such problems can often be reduced to maximizing a submodular set function subject to various constraints. Classical approaches to submodular optimization require centralized access to the full dataset, which is impractical for truly large-scale problems. In this paper, we consider the problem of submodular function
arXiv:1411.0541v2
fatcat:argsl5avqrdnzin64rype6xmmm