tabular-anomaly-detection

This CIKM 2026 paper characterizes "convergence collapse" in fixed-target tabular anomaly detectors, where better optimization removes anomaly signal, and proposes Kernel Contraction Matching (KCM), a closed-form kernel-regression detector, plus the Kernel-Anchored Regularizer (KAR) to mitigate collapse and improve AUROC on ADBench.

This paper explores adapting prior-data fitted networks (PFNs) for tabular anomaly detection, proposing a fine-tuning-free approach (ZEN) using frozen TabPFN features with nearest-neighbor scoring, and a fine-tuned method (FOCUS) that surpasses all baselines on the ADBench benchmark.