AI RESEARCH
Calibrating Tabular Anomaly Detection via Optimal Transport
arXiv CS.LG
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ArXi:2602.06810v2 Announce Type: replace Tabular anomaly detection (TAD) remains challenging due to the heterogeneity of tabular data: features lack natural relationships, vary widely in distribution and scale, and exhibit diverse types. Consequently, each TAD method makes implicit assumptions about anomaly patterns that work well on some datasets but fail on others, and no method consistently outperforms across diverse scenarios.