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