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Anomaly detection is essential for preventing hazardous outcomes forsafety-critical applications like autonomous driving. Given theirsafety-criticality, these applications benefit from provable bounds on variouserrors in anomaly detection. To achieve this goal in the semi-supervisedsetting, we propose to provide Probably Approximately Correct (PAC) guaranteeson the false negative and false positive detection rates for anomaly detectionalgorithms. Our method (PAC-Wrap) can wrap around virtually any existingsemi-supervised and unsupervised anomaly detection method, endowing it withrigorous guarantees. Our experiments with various anomaly detectors anddatasets indicate that PAC-Wrap is broadly effective.

PAC-Wrap: Semi-Supervised PAC Anomaly Detection