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Poster

Consistent Submodular Maximization

PAUL DUETTING · Federico Fusco · Silvio Lattanzi · Ashkan Norouzi-Fard · Morteza Zadimoghaddam

Hall C 4-9
[ ]
Wed 24 Jul 2:30 a.m. PDT — 4 a.m. PDT

Abstract:

Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper, we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion, and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i.e., the number of changes between two consecutive solutions is bounded). In this setting, we provide algorithms with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances.

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