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

Abstract

Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, 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… (More)
DOI: 10.1613/jair.3278

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