Horizontally Scalable Submodular Maximization

Mario Lucic; Olivier Bachem; Morteza Zadimoghaddam; Andreas Krause

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arXiv · 1605.09619

Horizontally Scalable Submodular Maximization

Mario Lucic·Olivier Bachem·Morteza Zadimoghaddam·Andreas Krause

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Abstract

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. In practice, while one can provision many machines, the capacity of each machine is limited by physical constraints. We propose a truly scalable approach for distributed submodular maximization under fixed capacity. The proposed framework applies to a broad class of algorithms and constraints and provides theoretical guarantees on the approximation factor for any available capacity. We empirically evaluate the proposed algorithm on a variety of data sets and demonstrate that it achieves performance competitive with the centralized greedy solution.

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BibTeX RIS

Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause. 2016-05-31. Horizontally Scalable Submodular Maximization. https://arxiv.org/abs/1605.09619

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