论文标题
PAC卷：半监督PAC异常检测
PAC-Wrap: Semi-Supervised PAC Anomaly Detection
论文作者
论文摘要
异常检测对于预防自动驾驶（例如自动驾驶）危险结果至关重要。鉴于它们的安全至关重要，这些应用程序受益于在异常检测中的各种错误中的可证明的界限。为了在半监督的环境中实现这一目标，我们建议在虚假的阴性和假阳性检测率上为异常检测算法提供大约正确的（PAC）保证。我们的方法（Pac-wrap）几乎可以围绕任何现有的半监督和无监督的异常检测方法，并具有严格的保证。我们对各种异常检测器和数据集进行的实验表明，PAC包装具有广泛的有效性。
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.
