Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning that can help address this issue using a ”human-in-the-loop” approach. In this interactive setting, the algorithm asks the user to label a query data point under an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to bring to the user’s attention a representative data point from each category in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as rare category detection. We prese...