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From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection
arXiv:2602.03018v2 Announce Type: replace Abstract: Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes algorithm and hyperparameter selection notoriously hard. Foundation models (FMs) have transformed ML, and OD is no exception: (2025) introduced FoMo-0D, the first FM for OD, achieving remarkable performance against numerous baselines.