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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…
cs.LG2024
RPS: A Generic Reservoir Patterns Sampler
Lamine Diop, Marc Plantevit, Arnaud Soulet
Efficient learning from streaming data is important for modern data analysis due to the continuous and rapid evolution of data streams. Despite significant advancements in stream p…