---
title: "利用近似留一法加速共形预测"
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: ["不确定性量化","共形预测","算法加速","近似留一法"]
---

# 利用近似留一法加速共形预测

## 速览

共形预测为预测推理提供不确定性量化框架，但计算成本高。本文引入近似留一法（ALO）估计量进一步加速共形预测，并建立渐近覆盖率和效率的理论保证。模拟结果显示，基于ALO的方法在保持与精确方法相当的覆盖率和效率的同时，显著减少运行时间。

## 关键点

- 提出基于近似留一法的加速共形预测方法
- 建立渐近覆盖率和效率的理论证明
- 模拟实验验证ALO方法在保持性能同时显著减少计算时间

## 全文

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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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_022b70406a83c8f906b21973