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arXiv Machine Learning By Jie Deng

When Identical Rows Disagree: From Benchmark Identifiability to Replication-Robust Anomaly Detection

Read the original on arXiv Machine Learning →

The paper investigates how repeated rows in released datasets—often treated as i.i.d. samples—introduce a hidden measurement layer that affects anomaly detection. It shows that identical rows can cap evaluation performance, make AUROC sensitive to replication, and bias detectors toward multiplicity size. The authors audit 690 OddBench datasets, find significant train-test overlap and label conflicts, and propose SCOUT, a support‑count orthogonalized detector that separates replication‑invariant evidence from exposure‑aware counts, achieving comparable or better AUROC while controlling false‑positive rates.

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