Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Existing anomaly detection paradigms overwhelmingly focus on training detection models
using exclusively normal data or unlabeled data (mostly normal samples). One notorious …

Anomaly explanation, also known as anomaly localization, is as important as, if not more
than, anomaly detection in many real-world applications. However, it is challenging to build …

Current techniques for explaining outliers cannot tell what caused the outliers. We present a
formal method to identify" root causes" of outliers, amongst variables. The method requires a …

While many techniques for outlier detection have been proposed in the literature, the
interpretation of detected outliers is often left to users. As a result, it is difficult for users to …

Abstract Explainable Artificial Intelligence (XAI) develops technical explanation methods and
enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine …

Anomaly detection has been studied intensively by the data mining community for several
years. As a result, many methods to detect anomalies have emerged, and others are still …

Outlier detection is an important task in many domains and is intensively studied in the past
decade. Further, how to explain outliers, ie, outlier interpretation, is more significant, which …