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PAC greedy maximization with efficient bounds on information gain for sensor selection

Abstract:

Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is computationally feasible to evaluate F , the function being maximized. Unfortunately, in many realistic settings F is too expensive to evaluate exactly even once. We present probably approximately correct greedy maximization, which requires access only to cheap anytime confidence bounds on F and uses them to prune el...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Oliehoek, FA More by this author
Satsangi, Y More by this author
Publisher:
International Joint Conference on Artificial Intelligence Publisher's website
Publication date:
2016-04-05
URN:
uuid:ffced9d2-05c1-4014-9480-b7c57f84d271
Source identifiers:
616630
Local pid:
pubs:616630

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