AI RESEARCH
ST-BCP: Tightening Coverage Bound for Backward Conformal Prediction via Non-Conformity Score Transformation
arXiv CS.LG
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ArXi:2602.01733v2 Announce Type: replace-cross Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees. While CP yields uncontrolled prediction set sizes, Backward Conformal Prediction (BCP) inverts this paradigm by enforcing a predefined upper bound on set size and estimating the resulting coverage guarantee. However, the looseness induced by Marko's inequality within the BCP framework causes a significant gap between the estimated coverage bound and the empirical coverage. In this work, we