Accelerating Conformal Prediction via Approximate Leave-One-Out

Statistics > Machine Learning

arXiv:2606.31915v1 (stat) [Submitted on 30 Jun 2026]

Title:Accelerating Conformal Prediction via Approximate Leave-One-Out

Authors:Jiachen Cong, Jingbo Liu View a PDF of the paper titled Accelerating Conformal Prediction via Approximate Leave-One-Out, by Jiachen Cong and Jingbo Liu View PDF HTML (experimental)

Abstract:While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost. Recent methods, including Jackknife+ and Jackknife-minmax, achieve faster computation by trading a slight loss of efficiency relative to full conformal prediction, but still requires computing leave-one-out refits for all observations. In this paper, we further accelerate conformal prediction by incorporating approximate leave-one-out (ALO) estimators, and establish asymptotic coverage and efficiency. While our proof draws on methods developed for analyzing the consistency of ALO cross-validation risk estimators in high-dimensional statistics, it requires adaptations to handle conformal prediction, where leave-$i$-out residuals are needed for predictions at $x_{n+1}$ rather than just at the training covariate $x_i$. Simulation results validate our theoretical findings, showing that the ALO-based methods achieve coverage and efficiency comparable to the exact methods, while significantly reducing the runtime.

Subjects:	Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as:	arXiv:2606.31915 [stat.ML]
	(or arXiv:2606.31915v1 [stat.ML] for this version)
	https://doi.org/10.48550/arXiv.2606.31915 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Jiachen Cong [view email]
[v1] Tue, 30 Jun 2026 16:21:48 UTC (32 KB)
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