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Conformal prediction with localization
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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.
Forward citations
Cited by 2 Pith papers
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Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction
For randomly localized conformal prediction, high-probability bounds, uniform over a realized neighborhood, control conditional coverage error and oracle-relative length at rate h^beta + sqrt(log(1/delta)/(n h^d)) plu...
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Robust Bayesian Optimization via Localized Online Conformal Prediction
LOCBO calibrates the GP likelihood with localized online conformal prediction and then denoises it, claiming utility lower bounds that are not actually established for the expected-improvement setting.
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