Abstract: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. We propose \emph{Online Localized Conformal Prediction (OLCP)}, which combines online adaptation with covariate-dependent localization to better reflect heterogeneity. To reduce sensitivity to the localization bandwidth, we further develop \emph{OLCP-Hedge}, which performs bandwidth selection as an online expert aggregation problem using a constrained online convex optimization framework. Importantly, we provide coverage guarantees for both algorithms and demonstrate through simulations and real-data experiments that the proposed methods attain valid long-run coverage with narrower prediction sets than existing baselines.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.05497 [cs.LG] |
| (or arXiv:2605.05497v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05497 arXiv-issued DOI via DataCite (pending registration) |
From: Yuheng Lai [view email]
[v1]
Wed, 6 May 2026 22:43:18 UTC (558 KB)
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