Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Evaluating Language Model Features for Zero-Shot Time-Series Anomaly Detection Under Isolation Assumption
Romain Hermary, Nesryne Mejri, Djamila Aouada
Time-Series Anomaly Detection (TSAD) is a fundamentally challenging task: it is unsupervised, requires high local precision capabilities, and suffers from limited labelled data. Re…
cs.LG2026
An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
Romain Hermary, Nesryne Mejri, Djamila Aouada
Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics such as AUROC, AUPR, F1-score…
cs.LG2026
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
Romain Hermary, Samet Hicsonmez, Dan Pineau +2
Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous a…