Multi-source conformal prediction: leveraging heterogeneity via localization
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 resp...
| Authors | Rohan Hore, Anirban Chatterjee, Sayantan Choudhury |
|---|---|
| Primary category | stat.ML |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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