Uncertainty Quantification in Anomaly Detection with Cross-Conformal $p$-Values
<|reference_start|>Uncertainty Quantification in Anomaly Detection with Cross-Conformal $p$-Values: Given the growing significance of reliable, trustworthy, and explainable machine learning, the requirement of uncertainty quantification for anomaly detection systems has become increasingly important. In this context, effectively controlling Type I error rates ($\alpha$) without compromising the statistical power ($1-\beta$) of these systems can build trust and reduce costs related to false discoveries, particularly when follow-up procedures are expensive. Leveraging the principles of conformal prediction emerges as a promising approach for providing respective statistical guarantees by calibrating a model's uncertainty. This work introduces a novel framework for anomaly detection, termed cross-conformal anomaly detection, building upon well-known cross-conformal methods designed for prediction tasks. With that, it addresses a natural research gap by extending previous works in the context of inductive conformal anomaly detection, relying on the split-conformal approach for model calibration. Drawing on insights from conformal prediction, we demonstrate that the derived methods for calculating cross-conformal $p$-values strike a practical compromise between statistical efficiency (full-conformal) and computational efficiency (split-conformal) for uncertainty-quantified anomaly detection on benchmark datasets.<|reference_end|>
@article{hennhöfer2024uncertainty,
    title={Uncertainty Quantification in Anomaly Detection with Cross-Conformal
  $p$-Values},
    author={Oliver Hennh"ofer and Christine Preisach},
    journal={arXiv preprint arXiv:2402.16388},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16388},
    primaryClass={stat.ML cs.LG}
}