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.
翻译:异常检测对于防止诸如自动驾驶等安全关键应用的危险结果至关重要。 由于其安全临界性,这些应用受益于异常检测中各种错误的可辨别界限。 为了在半监督环境下实现这一目标,我们提议为异常检测算法的虚假反向和假正向检测率提供可能大致正确(PAC)的保证。 我们的方法(PAC-Wrap)可以覆盖几乎所有现有的半监督和未经监督的异常检测方法,并给予严格的保障。 我们用各种异常检测器和数据集进行的实验表明,PAC-Wrap(PAC-Wrap)大致有效。