2 papers
cs.LG2026
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
Wei Huang, Hezhe Qiao, Kailai Zhang +3
Unsupervised tabular anomaly detection methods typically learn feature patterns from normal samples during training and subsequently identify samples that deviate from these patter…
cs.AI2026
Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation
Wei Huang, Yuxuan Xiong, Hezhe Qiao +3
Identifying anomalous instances in tabular data is essential for improving data reliability and maintaining system stability. Due to the scarcity of ground-truth anomaly labels, ex…