Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Anomaly-Free Self-Optimization via AUC Bounds
Kevin Wilkinghoff, Zheng-Hua Tan
Anomalies are rare, and anomalous data are often unavailable during development, making it difficult to determine which anomaly detection models and configurations will generalize…
cs.LG2026
Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?
Kevin Wilkinghoff, Zheng-Hua Tan
Anomaly detection systems are often trained using normal data alone, while model selection and evaluation typically require labeled anomalies. We study whether anomaly detection pe…
cs.LG2026
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6
Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on norm…