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Poster
Monotone k-Submodular Function Maximization with Size Constraints
Naoto Ohsaka · Yuichi Yoshida

Mon Dec 07 04:00 PM -- 08:59 PM (PST) @ 210 C #77 #None
A $k$-submodular function is a generalization of a submodular function, where the input consists of $k$ disjoint subsets, instead of a single subset, of the domain.Many machine learning problems, including influence maximization with $k$ kinds of topics and sensor placement with $k$ kinds of sensors, can be naturally modeled as the problem of maximizing monotone $k$-submodular functions.In this paper, we give constant-factor approximation algorithms for maximizing monotone $k$-submodular functions subject to several size constraints.The running time of our algorithms are almost linear in the domain size.We experimentally demonstrate that our algorithms outperform baseline algorithms in terms of the solution quality.

Author Information

Naoto Ohsaka (The University of Tokyo)
Yuichi Yoshida (National Institute of Informatics and Preferred Infrastructure, Inc.)

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