Cost Effective Active Search

We study a paradigm of active learning we call cost effective active search, where the goal is to find a given number of positive points from a large unlabeled pool with minimum labeling cost. Most existing methods solve this problem heuristically, and few theoretical results have been established. Here we adopt a principled Bayesian approach for the first time.