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Mar, 2010
自适应次模性：主动学习和随机优化中的理论和应用
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
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Daniel Golovin, Andreas Krause
TL;DR
本文提出自适应子模性的概念，将子模集函数推广到自适应策略，并使用自适应贪心算法解决具有不确定性结果的随机优化问题，通过使用懒惰评估方法显著加快了算法。通过提供子模目标的几个示例，包括传感器放置，病毒营销和主动学习，证明了自适应子模性的实用性。
Abstract
Solving
stochastic optimization
problems under partial observability, where we need to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of
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