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. The proposed method is a natural extension to conformal inference. We prove that our proposal can also have assumption-free and finite sample coverage guarantees, and we compare the behaviors of localized conformal inference and conformal inference in simulations. To our knowledge, this is the first work that 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.
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