Anomaly detection is widely used in real-world applications such as industrial fault detection, quality control, cybersecurity, fraud detection, healthcare monitoring, and scientific data analysis. In these scenarios, abnormal patterns are often rare but critical. However, practical anomaly detection is challenging because real-world data are usually noisy, incomplete, high-dimensional, graph-structured, or collected from heterogeneous domains, while reliable anomaly labels are often limited or unavailable.

This talk presents a line of research on robust learning for anomaly detection in complex and imperfect data. I will discuss methods for tabular anomaly detection under noise and missing values, graph-level anomaly detection, automatic hyper-parameter optimization, semi-supervised anomaly detection, and universal outlier detection across diverse domains. Together, these works aim to develop anomaly detection methods that are robust, adaptive, and generalizable for real-world applications.