Targeted Active Learning for Bayesian Decision-Making

• URL: http://arxiv.org/abs/2106.04193v1
• Date: Tue, 8 Jun 2021 09:05:43 GMT
• Title: Targeted Active Learning for Bayesian Decision-Making
• Authors: Louis Filstroff, Iiris Sundin, Petrus Mikkola, Aleksei Tiulpin, Juuso Kylm\"aoja, Samuel Kaski
• Abstract summary: We argue that when acquiring samples sequentially, separating learning and decision-making is sub-optimal. We introduce a novel active learning strategy which takes the down-the-line decision problem into account. Specifically, we introduce a novel active learning criterion which maximizes the expected information gain on the posterior distribution of the optimal decision.
• Score: 15.491942513739676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 a novel active learning strategy which takes the down-the-line decision problem into account. Specifically, we introduce a novel active learning criterion which maximizes the expected information gain on the posterior distribution of the optimal decision. We compare our decision-making-aware active learning strategy to existing alternatives on both simulated and real data, and show improved performance in decision-making accuracy.

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