Submodular learning and covering with response-dependent costs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for response-dependent costs. We bound the approximation factor of this greedy algorithm in active learning settings as well as in the general setting. We show that a different property of the cost function controls the approximation factor in each of these scenarios. We further show that in both settings, the approximation factor of this greedy algorithm is near-optimal among all greedy algorithms. Experiments demonstrate the advantages of the proposed algorithm in the response-dependent cost setting.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings
EditorsHans Ulrich Simon, Sandra Zilles, Ronald Ortner
PublisherSpringer Verlag
Pages130-144
Number of pages15
ISBN (Print)9783319463780
DOIs
StatePublished - 1 Jan 2016
Event27th International Conference on Algorithmic Learning Theory, ALT 2016 - Bari, Italy
Duration: 19 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9925 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Algorithmic Learning Theory, ALT 2016
Country/TerritoryItaly
CityBari
Period19/10/1621/10/16

Keywords

  • Interactive learning
  • Outcome costs
  • Submodular functions

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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