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cs.LG2026
uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN
Sha Lu, Jixue Liu, Stefan Peters +4
Anomaly detection in tabular data is challenging due to high dimensionality, complex feature dependencies, and heterogeneous noise. Many existing methods rely on proximity-based cu…
cs.LG2020★ 3 cited
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
Sha Lu, Lin Liu, Kui Yu +3
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a…