Anomaly detection is a fundamental data analytics task across scientific fields and
industries. In recent years, an increasing interest has been shown in the application of …

Tabular data have been playing a mostly important role in diverse real-world fields, such as
healthcare, engineering, finance, etc. With the recent success of deep learning, many …

We propose a modified density estimation problem that is highly effective for detecting
anomalies in tabular data. Our approach assumes that the density function is relatively …

Anomaly detection plays a vital role in various realistic applications, including fraud
detection, network traffic analysis, medical diagnosis, and so on. Semi-supervised anomaly …

Due to the scarcity of reliable anomaly labels, recent anomaly detection methods leveraging
noisy auto-generated labels either select clean samples or refurbish noisy labels. However …

Anomaly detection is a crucial data mining problem due to its extensive range of
applications. In real-world scenarios, anomalies often exhibit different levels of priority …

Tabular anomaly detection, which aims at identifying deviant samples, has been crucial in a
variety of real-world applications, such as medical disease identification, financial fraud …