  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
introduce a novel active learning criterion which 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.
