Targeted Active Learning for Bayesian Decision-Making
Abstract: Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way. However, maximizing the accuracy is not the end goal when the results are used for decision-making, for example in personalized medicine or economics. We argue that when acquiring samples sequentially, separating learning and decision-making is sub-optimal, and we introduce an active learning strategy which takes the down-the-line decision problem into account. Specifically, we adopt a Bayesian experimental design approach, and the proposed criterion maximizes the expected information gain on the posterior distribution of the optimal decision. We compare our targeted active learning strategy to existing alternatives on both simulated and real data, and show improved performance in decision-making accuracy.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Roman_Garnett1
Submission Number: 1480
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