  Selecting informative data points for expert feedback can significantly
improve the performance of anomaly detection (AD) in various contexts, such as
medical diagnostics or fraud detection. In this paper, we determine a set of
theoretical conditions under which anomaly scores generalize from labeled
queries to unlabeled data. Motivated by these results, we propose a data
labeling strategy with optimal data coverage under labeling budget constraints.
In addition, we propose a new learning framework for semi-supervised AD.
Extensive experiments on image, tabular, and video data sets show that our
approach results in state-of-the-art semi-supervised AD performance under
labeling budget constraints.
