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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CLAPS integrates heteroscedastic last-layer Laplace uncertainty as a local scale in split conformal regression to produce intervals that adapt to both aleatoric and epistemic uncertainty while preserving nominal coverage.
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.
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A Kernel Nonconformity Score for Multivariate Conformal Prediction
A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.