Uncertainty Quantification

Operational, Uncertainty-Aware, and Reliable Anomaly Detection

Anomaly detection methods aim to identify examples that do not follow the expected behavior. For various reasons, anomaly detection is typically tackled by using unsupervised approaches that assign real-valued anomaly scores based on various …

How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection @ AAAI23

Anomaly detection attempts at finding examples that deviate from the expected behaviour. Usually, anomaly detection is tackled from an unsupervised perspective because anomalous labels are rare and difficult to acquire. However, the lack of labels …

Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions @ Polito