When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination

Title:When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination

Abstract:Trimming suspicious calibration points is a common response to contamination in conformal prediction. Its effect on clean-target coverage, however, is governed by the retained law induced by trimming, not by the contamination level alone. We analyse fixed-threshold trimming as conditioning rather than purification. It replaces the contaminated calibration law with a retained law, reducing clean-target coverage to a one-dimensional score-CDF transfer problem with an exact finite-sample identity. A componentwise bound on the transfer gap gives a population-level diagnostic. This separates a clean-side covariance cost from a retained-contamination cost, governed by the dirty-to-clean retention ratio. Trimming helps when the anomaly score separates retention probabilities while remaining score-neutral on the clean population. Otherwise, it cannot substantially reduce contamination through the retained mixture coefficient. We also give finite-sample certificate templates that provide numerical guarantees under independent audit.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2605.06204 [stat.ML]

(or arXiv:2605.06204v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2605.06204 Focus to learn more arXiv-issued DOI via DataCite

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