---
title: "Accelerating Conformal Prediction via Approximate Leave-One-Out"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.31915v1"
permalink: "https://feed.astralor.com/items/itm_022b70406a83c8f906b21973"
published: "2026-06-30T16:21:48.000Z"
score: 53
channel: "ai"
tags: ["Uncertainty Quantification","Conformal Prediction","Algorithm Acceleration","approximate leave-one-out"]
---

# Accelerating Conformal Prediction via Approximate Leave-One-Out

## At a glance

Conformal prediction provides a general framework for uncertainty quantification in predictive inference, but its application is often limited by computational cost. This paper further accelerates conformal prediction by incorporating approximate leave-one-out (ALO) estimators, establishing theoretical guarantees for asymptotic coverage and efficiency. Simulation results show that ALO-based methods achieve comparable coverage and efficiency to exact methods while significantly reducing runtime.

## Key points

- Proposed accelerated conformal prediction methods using approximate leave-one-out (ALO) estimators
- Provided theoretical proofs for asymptotic coverage and efficiency
- Simulation results confirm that ALO methods significantly reduce runtime while maintaining performance

## Full text

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.

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