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Data Summarization Beyond Monotonicity: Non-monotone Two-Stage Submodular Maximization

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Abstract

The objective of a two-stage submodular maximization problem is to reduce the ground set using provided training functions that are submodular, with the aim of ensuring that optimizing new objective functions over the reduced ground set yields results comparable to those obtained over the original ground set. This problem has applications in various domains including data summarization. Existing studies often assume the monotonicity of the objective function, whereas our work pioneers the extension of this research to accommodate non-monotone submodular functions. We have introduced the first constant-factor approximation algorithms for this more general case.

Original languageEnglish
Title of host publicationCombinatorial Optimization and Applications - 16th International Conference, COCOA 2023, Proceedings
EditorsWeili Wu, Jianxiong Guo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages277-286
Number of pages10
ISBN (Print)9783031496103
DOIs
StatePublished - 2024
Event16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023 - Hawai, United States
Duration: Dec 15 2023Dec 17 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14461 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023
Country/TerritoryUnited States
CityHawai
Period12/15/2312/17/23

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