AI RESEARCH
Conformal Prediction Assessment: A Framework for Conditional Coverage Evaluation and Selection
arXiv CS.LG
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ArXi:2603.27189v1 Announce Type: cross Conformal prediction provides rigorous distribution-free finite-sample guarantees for marginal coverage under the assumption of exchangeability, but may exhibit systematic undercoverage or overcoverage for specific subpopulations. Assessing conditional validity is challenging, as standard stratification methods suffer from the curse of dimensionality. We propose Conformal Prediction Assessment (CPA), a framework that reframes the evaluation of conditional coverage as a supervised learning task by.