提出数据驱动的ARGOS指标，自动选最敏感的异常检测模型。
How to pick the best anomaly detector?
- 基于弱监督分类器，用数据驱动方法选择最佳异常检测模型。
- 在噪声环境下，ARGOS比交叉熵等传统指标更稳定有效。
- 适用于超参调优、架构与特征选择，适合高能物理等实际场景。
异常检测有望在数据未探索区域发现新物理现象。然而，以模型无关方式为特定数据集选择最优异常检测器，仍是重要挑战且长期被忽视。本文提出基于数据驱动的ARGOS指标，具有坚实的理论基础，并实证表明其能稳健选出对异常最敏感的检测模型。聚焦于弱监督、基于分类器的异常检测方法，我们证明ARGOS优于文献中常用的其他模型选择指标，特别是二元交叉熵损失。通过多个真实应用场景（包括超参数调优、模型结构与特征选择），我们验证了在异常检测的噪声条件下，ARGOS仍具高度鲁棒性。
原文摘要 · Abstract (English)
Anomaly detection has the potential to discover new physics in unexplored regions of the data. However, choosing the best anomaly detector for a given data set in a model-agnostic way is an important challenge which has hitherto largely been neglected. In this paper, we introduce the data-driven ARGOS metric, which has a sound theoretical foundation and is empirically shown to robustly select the most sensitive anomaly detection model given the data. Focusing on weakly-supervised, classifier-based anomaly detection methods, we show that the ARGOS metric outperforms other model selection metrics previously used in the literature, in particular the binary cross-entropy loss. We explore several realistic applications, including hyperparameter tuning as well as architecture and feature selection, and in all cases we demonstrate that ARGOS is robust to the noisy conditions of anomaly detection.
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