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Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization

Golovin, Daniel and Krause, Andreas (2011) Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. Journal of Artificial Intelligence Research, 42 . pp. 427-486. ISSN 1076-9757. https://resolver.caltech.edu/CaltechAUTHORS:20120104-101318653

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Abstract

Many problems in artificial intelligence require adaptively making a sequence of decisions with uncertain outcomes under partial observability. Solving such stochastic optimization problems is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse AI applications including management of sensing resources, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1613/jair.3278DOIUNSPECIFIED
http://www.jair.org/papers/paper3278.htmlPublisherUNSPECIFIED
Additional Information:© 2011 AI Access Foundation. Submitted 1/11; published 11/11. An extended abstract of this work appeared in COLT 2010 (Golovin & Krause, 2010). We wish to thank the anonymous referees for their helpful suggestions. This research was partially supported by ONR grant N00014-09-1-1044, NSF grant CNS-0932392, NSF grant IIS-0953413, DARPA MSEE grant FA8650-11-1-7156, a gift by Microsoft Corporation, an Okawa Foundation Research Grant, and by the Caltech Center for the Mathematics of Information.
Funders:
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-09-1-1044
NSFCNS-0932392
NSFIIS-0953413
DARPA MSEEFA8650-11-1-7156
Microsoft CorporationUNSPECIFIED
Okawa Foundation Research GrantUNSPECIFIED
Caltech Center for Mathematics of InformationUNSPECIFIED
Record Number:CaltechAUTHORS:20120104-101318653
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20120104-101318653
Official Citation:D. Golovin and A. Krause (2011) "Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization", Volume 42, pages 427-486
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:28643
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:04 Jan 2012 19:38
Last Modified:03 Oct 2019 03:34

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