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.
@article{arxiv.2205.10798,
title = {PAC-Wrap: Semi-Supervised PAC Anomaly Detection},
author = {Shuo Li and Xiayan Ji and Edgar Dobriban and Oleg Sokolsky and Insup Lee},
journal= {arXiv preprint arXiv:2205.10798},
year = {2022}
}