文章基本信息

标题：Near-optimal Bayesian active learning with correlated and noisy tests
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作者：Yuxin Chen ; S. Hamed Hassani ; Andreas Krause 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2017
卷号：11
期号：2
页码：4969-5017
DOI：10.1214/17-EJS1336SI
语种：English
出版社：Institute of Mathematical Statistics
摘要：We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests. In contrast to prior work, we focus on the challenging, yet practically relevant setting where test outcomes can be conditionally dependent given the hidden target variable. Under such assumptions, common heuristics, such as greedily performing tests that maximize the reduction in uncertainty of the target, often perform poorly.