arXiv Machine Learning July 27, 2026 By Oliver Hennh\"ofer, Maximilian Kirsch, Christine Preisach

Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with nonconform

Read the original on arXiv Machine Learning →

arXiv:2605. 13642v2 Announce Type: replace-cross Abstract: Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without clear statistical interpretation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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arXiv:2402. 16388v4 Announce Type: replace-cross Abstract: The need for uncertainty quantification in anomaly detection systems has become increasingly important.

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arXiv Machine Learning

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