SOURCE-LINKED INTELLIGENCE

Anomaly-Free Self-Optimization via AUC Bounds

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

Anomalies are rare, and anomalous data are often unavailable during development, making it difficult to determine which anomaly detection models and configurations will generalize to unseen anomalies. Recent approaches address this challenge by generating pseudo-anomalies and using bounds on the achievable area under the ROC curve (AUC) to select the optimal configuration from a finite set of candidates. Instead, we use the AUC bound as a differentiable, anomaly-free objective for directly optimizing continuous parameters of anomaly detection systems. We demonstrate this framework by optimizin

Read original source ↗ Open in workspace

recordType

paper

region

Global