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Multi-source conformal prediction: leveraging heterogeneity via localization — arXiv:2609.14531

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14531
License (as archived): CC0-1.0
Archived: 2026-10-16T00:15:34.881Z
SHA-256 of the archived text: 852a6970fd4d61cd38ef3038e55d454d197b7c79e0102e16ed514a2dcefe1609
Stable URL of this page: https://metraipolis.com/documento/dc_4691be21e80f

How to cite

Multi-source conformal prediction: leveraging heterogeneity via localization — arXiv:2609.14531 — arXiv (en), https://arxiv.org/abs/2609.14531 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4691be21e80f (Metraipolis archive dc_4691be21e80f, sha256 852a6970fd4d61cd38ef3038e55d454d197b7c79e0102e16ed514a2dcefe1609)

BibTeX

@misc{dc4691be21e80f,
  title = {Multi-source conformal prediction: leveraging heterogeneity via localization — arXiv:2609.14531},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14531},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4691be21e80f; sha256 852a6970fd4d61cd38ef3038e55d454d197b7c79e0102e16ed514a2dcefe1609},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14531. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.14531 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T23:54:14.911Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).