arXiv:2402. 16388v4 Announce Type: replace-cross Abstract: The need for uncertainty quantification in anomaly detection systems has become increasingly important.
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.
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition introduces C-PP-COAD, a framework that uses synthetic calibration data to reduce reliance on real-world calibration while maintaining assumption-free false discovery rate control. The method wraps any anomaly detection algorithm, converting its scores into conformal p-values for online testing. Experiments on synthetic and real datasets—including thyroid dysfunction, O‑RAN conflict, 5G intrusion, and UE throughput degradation—show that C-PP-COAD preserves FDR guarantees while significantly cutting the need for real calibration data.
arXiv:2605. 20726v2 Announce Type: replace-cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold.
arXiv:2601. 02610v3 Announce Type: replace-cross Abstract: Novelty detection via conformal $p$-values and BH procedure provides distribution-free global false discovery rate (FDR) control.
arXiv:2606. 03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits.
arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
The paper investigates whether the performance of anomaly detection systems can be predicted without labeled anomalies. For kNN-based detectors, it derives a lower bound on AUC that links detection performance to the separation and variance of inlier and outlier scores, and uses this to analyze how density variation, intrinsic dimensionality, and domain mismatch affect score variability. The authors introduce pseudo‑anomaly probes that provide a reference for estimating relative score separation, and demonstrate through experiments on DCASE benchmarks that these probes enable anomaly‑free model selection to outperform conventional development‑set selection, especially under domain shift.
arXiv:2606. 13780v1 Announce Type: cross Abstract: Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it.
arXiv:2602. 01733v3 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees.
Conformal prediction replaces single-class predictions with prediction sets that guarantee a pre-specified coverage probability. The paper reviews properties of non‑conformity score functions, presents examples from the literature, and proposes new modifications. It introduces a method to evaluate prediction set sizes and compares different score functions, including their effectiveness for class‑conditional conformal prediction with imbalanced classes.
arXiv:2607. 26704v1 Announce Type: cross Abstract: Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies.
arXiv:2510. 05566v2 Announce Type: replace-cross Abstract: Large language models have achieved impressive performance across diverse tasks.