Trimming helps conformal prediction under contamination precisely when the anomaly score separates retention probabilities without biasing clean scores, otherwise the retained mixture coefficient prevents substantial decontamination.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=
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StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
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When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination
Trimming helps conformal prediction under contamination precisely when the anomaly score separates retention probabilities without biasing clean scores, otherwise the retained mixture coefficient prevents substantial decontamination.
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Stable Localized Conformal Prediction via Transduction
StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
- A Unified Theory of Conditional Coverage in Conformal Prediction with Applications
- Multi-Fidelity Quantile Regression