RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.
Unsupervised representation learning by predicting random distances.arXiv preprint arXiv:1912.12186,
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CTAD calibrates anomaly scores via optimal transport distance on empirical and K-means structural distributions of normal data, yielding consistent gains across 34 tabular datasets and seven detector types.
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RGLD: Randomized Global-Local Density Estimation for Tabular Anomaly Detection
RGLD combines randomized global and local density estimation over feature-bagged views to achieve top AUROC wins and strong AUPRC on 47 tabular datasets while running 50-580x faster than deep detectors.
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Calibrating Tabular Anomaly Detection via Optimal Transport
CTAD calibrates anomaly scores via optimal transport distance on empirical and K-means structural distributions of normal data, yielding consistent gains across 34 tabular datasets and seven detector types.