AI RESEARCH
SpeedCP: Fast Kernel-based Conditional Conformal Prediction
arXiv CS.LG
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ArXi:2509.24100v2 Announce Type: replace-cross Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framework of Gibbs, which leverages families of covariate shifts to provide approximate conditional conformal prediction intervals, an approach with strong theoretical promise, but with prohibitive computational cost.