Conformal Prediction under Partial Verification

Abstract:Conformal prediction provides prediction sets with finite-sample guarantees, but the label verification required for calibration can be expensive. We develop a partial verification method that returns exactly the same prediction sets as complete verification. We characterize calibration certificates, the verified information sufficient to determine the conformal threshold, and design a procedure that coordinates verification across calibration examples. For finite thresholds at high coverage, its verification cost is less than twice the minimum certificate cost when candidates are checked in order. Across retrieval, mathematical solutions, and configuration evaluation, it reduces verification cost by 15-82% compared with verifying calibration examples one at a time, while producing identical prediction sets.

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

arXiv:2610.10829 [stat.ML]

(or arXiv:2610.10829v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.10829 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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