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Mar, 2023
决策为中心学习的理想抽象
Ideal Abstractions for Decision-Focused Learning
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Michael Poli, Stefano Massaroli, Stefano Ermon, Bryan Wilder, Eric Horvitz
TL;DR
提出了一种通过识别和利用决策的实用结构,在机器学习系统中制定简化抽象的方法,该方法自动配置输出空间以最小化决策相关信息的损失,该方法通过减少所需的数据实现了更好的决策质量。
Abstract
We present a methodology for formulating simplifying
abstractions
in
machine learning
systems by identifying and harnessing the
utility structure
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