Kernel mean matching enhances risk estimation under spatial distribution shifts

Abstract Accurate risk estimation under distribution shifts is critical for deploying machine learning models in real-world spatial applications, from ecological forecasting to medical image analysis. Conventional methods such as No Weighting (NW) and Importance Weighting (IW) fail in spatially stru...

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Bibliografski detalji

Glavni autori:	Egor Serov, Diana Koldasbayeva, Alexey Zaytsev
Format:	Artigo
Jezik:	Inglês
Izdano:	Nature Portfolio 2026-02-01
Serija:	Scientific Reports
Teme:	Kernel mean matching Spatial risk estimation Spatial modeling Importance reweighting Distribution shift robustness
Online pristup:	https://doi.org/10.1038/s41598-026-36740-7
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