Localized conformal model selection

Yuhao Wang; Tengyao Wang

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arXiv · 2602.19284

Localized conformal model selection

Yuhao Wang·Tengyao Wang

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Abstract

We propose a localized conformal model selection framework that integrates local adaptivity with post-selection validity for distribution-free prediction. By performing model selection symmetrically across calibration points using upper and lower surrogate intervals, we construct a data-dependent safe index set that contains the oracle model and preserves exchangeability. The resulting ensemble procedure retains exact finite-sample marginal coverage while adapting to spatial heterogeneity and model complexity. Simulations demonstrate substantial reductions in interval length compared to the best fixed model, especially in heterogeneous and low-noise settings.

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Yuhao Wang, Tengyao Wang. 2026-02-22. Localized conformal model selection. https://arxiv.org/abs/2602.19284

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