Targeted Active Learning for Bayesian Decision-Making

TL;DR

This paper proposes a novel active learning strategy that directly optimizes for decision-making accuracy by maximizing expected information gain on the posterior of the optimal decision, improving outcomes in practical applications.

Contribution

It introduces a new active learning criterion tailored for decision-making tasks, integrating the decision process into the sample acquisition strategy.

Findings

Outperforms existing active learning methods in decision accuracy

Demonstrates effectiveness on both simulated and real datasets

Enhances sample efficiency for decision-making tasks

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 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.