Conformal prediction with localization

Abstract

We propose a new method called localized conformal prediction, where we can perform conformal inference using only a local region around a new test sample to construct its confidence interval. Localized conformal inference is a natural extension to conformal inference. It generalizes the method of conformal prediction to the case where we can break the data exchangeability, so as to give the test sample a special role. To our knowledge, this is the first work that introduces such a localization to the framework of conformal prediction. We prove that our proposal can also have assumption-free and finite sample coverage guarantees, and we compare the behaviors of localized conformal prediction and conformal prediction in simulations.

arXiv comment19 pages, 4 figures

References in corpus (4)

• A tutorial on conformal prediction
• Adaptive, Distribution-Free Prediction Intervals for Deep Networks
• Cautious Deep Learning
• Flexible distribution-free conditional predictive bands using density estimators

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• MD-split+: Practical Local Conformal Inference in High Dimensions
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• AutoCP: Automated Pipelines for Accurate Prediction Intervals