Trustworthy Anomaly Detection

Professor Depeng Xu, UNC Charlotte

September 30, 2024. 12-1pm. WWH 335

Abstract:Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection. In the past decade, a variety of anomaly detection models have been developed, which lead to big progress towards accurately detecting various anomalies. Despite the successes, anomaly detection models still face many limitations. The most significant one is whether we can trust the detection results from the models. Considering that many anomaly detection tasks are life-changing tasks involving human beings, labeling someone as anomalies or fraudsters should be extremely cautious. Hence, ensuring the anomaly detection models conducted in a trustworthy fashion is an essential requirement to deploy the models to conduct automatic decisions in the real world. In this tutorial, we will introduce the existing efforts and discuss open problems towards trustworthy anomaly detection from the perspectives of explainability, fairness, robustness, and privacy-preservation.

Professor Depeng Xu is Assistant Professor in Software and Information Systems. His research focuses on data mining and machine learning, specifically differential privacy, algorithmic fairness, and ethical AI. He is also interested in robust and explainable machine learning in text classification, anomaly detection, and image recognition.