AI RESEARCH
Selective Conformal Risk Control
arXiv CS.LG
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ArXi:2512.12844v2 Announce Type: replace Reliable uncertainty quantification is essential for deploying machine learning systems in high-stakes domains. Conformal prediction provides distribution-free coverage guarantees but often produces overly large prediction sets, limiting its practical utility. To address this issue, we propose \textit{Selective Conformal Risk Control} (SCRC), a unified framework that integrates conformal prediction with selective classification.