The paper derives that calibration-conditional coverage follows a Beta(k, n+1-k) law under continuous i.i.d. exchangeability and quantifies non-i.i.d. departures via Wasserstein distances on transported beta laws, yielding explicit bounds in scale-shift, clustered, and mixing regimes.
arXiv preprint arXiv:2502.03609 , year=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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.
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.
Conformalized quantile regression applied post hoc to neutron star posterior samples yields reliable uncertainty bands validated by empirical coverage studies.
citing papers explorer
-
Conformal Prediction via Transported Beta Laws
The paper derives that calibration-conditional coverage follows a Beta(k, n+1-k) law under continuous i.i.d. exchangeability and quantifies non-i.i.d. departures via Wasserstein distances on transported beta laws, yielding explicit bounds in scale-shift, clustered, and mixing regimes.
-
Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
-
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.
-
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.
-
Conformal prediction for uncertainties in the neutron star equation of state
Conformalized quantile regression applied post hoc to neutron star posterior samples yields reliable uncertainty bands validated by empirical coverage studies.