Targeted Active Learning for Bayesian Decision-Making
This work addresses the inefficiency of separating learning and decision-making in active learning for practical applications like healthcare and economics, offering a more effective approach.
The paper tackles the problem of sub-optimal active learning when used for decision-making, such as in personalized medicine or economics, by introducing a targeted strategy that maximizes information gain on optimal decisions, showing improved decision-making accuracy in simulations and real data.
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.