An online KS-statistic monitor detects shifts in deployed safety classifiers with 86.6% valid detection rate, exposes conformal prediction collapse in high-dimensional embeddings, and derives a confidence-gated security boundary against adaptive attackers.
Reactive soft prototype computing for concept drift streams
2 Pith papers cite this work. Polarity classification is still indexing.
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Profile Drift Detection measures changes in partial dependence profiles with new metrics to detect concept drift while providing explanations and supporting efficient MLOps monitoring.
citing papers explorer
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Online Shift Detection and Conformal Adaptation for Deployed Safety Classifiers
An online KS-statistic monitor detects shifts in deployed safety classifiers with 86.6% valid detection rate, exposes conformal prediction collapse in high-dimensional embeddings, and derives a confidence-gated security boundary against adaptive attackers.
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From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection
Profile Drift Detection measures changes in partial dependence profiles with new metrics to detect concept drift while providing explanations and supporting efficient MLOps monitoring.