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cs.LG2026
Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data
Lamine Diop, Marc Plantevit
Reconstruction-based anomaly detectors are accurate but opaque: a deep autoencoder flags a sample without telling a practitioner which feature ranges made it anomalous. We propose…
cs.LG2026
Witnesses Explain Anomalies
Lamine Diop
Unsupervised anomaly detection scores each point of an unlabelled, contaminated sample in a single pass, and increasingly must also explain why a point is flagged. Yet the dominant…