# Localized conformal model selection

arXiv: https://arxiv.org/abs/2602.19284 · HTML: https://mlsanity.com/paper/2602.19284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2026.

## Built on by (most cited first, 2 of 2)

- [Multi-source conformal prediction: leveraging heterogeneity via localization](https://mlsanity.com/paper/2609.14531/) (2026)
- [Sequential Confidence Sets for Coverage-Constrained Conformal Model Selection](https://mlsanity.com/paper/2609.28522/) (2026)

## Abstract (arXiv)

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.