AI RESEARCH
Online Localized Conformal Prediction
arXiv CS.LG
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ArXi:2605.05497v1 Announce Type: new Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online learning and time-series settings, exchangeability is not satisfied. Existing online conformal methods, such as adaptive conformal inference (ACI), can achieve long-run validity, yet they remain inefficient under covariate heterogeneity because they rely on global calibration.