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Multi-source conformal prediction: leveraging heterogeneity via localization

arXiv 2609.14531 ↗First posted 13 Sep 2026stat.ML cs.LG stat.MEcited by 0

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Abstract (arXiv)

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.

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References · 39

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27 more
  1. 2021Localized Conformal Prediction: A Generalized Inference Framework for Conformal Predictioncited by 112
  2. 2023Class-Conditional Conformal Prediction with Many Classescited by 109
  3. 2019Distributional conformal predictioncited by 104
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  10. 2021Selection and Aggregation of Conformal Prediction Setscited by 46
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  18. 2024Conformal prediction after data-dependent model selectioncited by 13
  19. 2025Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coveragecited by 12
  20. 2024Multi-Source Conformal Inference Under Distribution Shiftcited by 11
  21. 2025Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbationscited by 11
  22. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 9
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  24. 2024Predictive Inference in Multi-environment Scenarioscited by 4
  25. 2026Multi-Distribution Robust Conformal Predictioncited by 1
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  27. 2026Unlocking Multi-Site Clinical Data: A Federated Approach to Privacy-First Child Autism Behavior Analysiscited by 1

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