AI RESEARCH
AdaptNC: Adaptive Nonconformity Scores for Conformal Prediction under Distribution Shift
arXiv CS.LG
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ArXi:2602.01629v2 Announce Type: replace Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function.