PIT-CP post-processes nonconformity scores via one-dimensional conditional density estimation to produce approximately pivotal scores, achieving approximate conditional coverage in conformal prediction for i.i.d. data.
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Multivariate standardized residuals via Mahalanobis distance from a learned local covariance yield asymptotic conditional coverage for conformal prediction under a derived sufficient condition on the data distribution.
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A Post-Processing Conformal Prediction Approach for Conditional Coverage via Pivotal Scores
PIT-CP post-processes nonconformity scores via one-dimensional conditional density estimation to produce approximately pivotal scores, achieving approximate conditional coverage in conformal prediction for i.i.d. data.
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Multivariate Standardized Residuals for Conformal Prediction
Multivariate standardized residuals via Mahalanobis distance from a learned local covariance yield asymptotic conditional coverage for conformal prediction under a derived sufficient condition on the data distribution.