Multi-source conformal prediction: leveraging heterogeneity via localization

2026-09-13

AI Summary

研究提出MS-RLCP方法，利用多源数据异质性通过自适应选择源来提高预测覆盖范围，适用于测试分布与源分布不同的情况。

Abstract

Many modern prediction tasks involve data from multiple heterogeneous sources, while the test distribution may differ substantially from any individual source. Although heterogeneity poses challenges, it also offers an opportunity: different sources may provide complementary information, with some regions of the feature space better represented in one source than another. We propose Multi-Source Randomly Localized Conformal Prediction (MS-RLCP), which builds on the local coverage properties of randomly localized conformal prediction (RLCP) (Hore and Barber, 2025) and extends it to multiple sources through data-adaptive source selection. Under the widely adopted assumption of a shared response distribution conditional on the features across sources and the test population, we establish finite-sample coverage bounds using an interpretable notion of envelope distribution that captures their aggregate feature-space representation. Our analysis allows the test feature distribution to be absolutely continuous with respect to the envelope, extending beyond mixtures of source distributions. Under additional regularity conditions, we also establish asymptotic test-conditional coverage. Simulations and real-world experiments demonstrate the effectiveness of MS-RLCP across varying levels of data heterogeneity.

Problem

• multi-source
• heterogeneity
• conformal prediction
• feature space
• coverage

Innovation

• Multi-Source Randomly Localized Conformal Prediction (MS-RLCP)
• heterogeneous data sources
• data-adaptive source selection
• envelope distribution
• coverage bounds