Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification
James A. Grant,
David S. Leslie
Abstract:We consider a variant of online binary classification where a learner sequentially assigns labels (0 or 1) to items with unknown true class. If, but only if, the learner chooses label 1 they immediately observe the true label of the item. The learner faces a trade-off between short-term classification accuracy and long-term information gain. This problem has previously been studied under the name of the 'apple tasting' problem. We revisit this problem as a partial monitoring problem with side information, and … Show more
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