Shadi Alijani

The work proposes Class-Aware Asymmetric Weighted Conformal Prediction (CA-WCP), which combines latent-space density-ratio weighting with directional quantiles that separately calibrate lower and upper volume bounds and scales each bound by a class-specific asymmetry factor derived from validation-set false-positive and false-negative rates; the authors prove it retains the weighted-exchangeability marginal coverage guarantee per class and show on BraTS 2020 and a covariate-shifted synthetic multi-organ CT benchmark that 95% Clopper–Pearson intervals for observed coverage contain the nominal 90% level for every class while interval width shrinks 8–14% versus symmetric weighted conformal prediction.

The work proposes Class-Aware Asymmetric Weighted Conformal Prediction (CA-WCP), which combines latent-space density-ratio weighting with directional quantiles that separately calibrate lower and upper volume bounds and scales each bound by a class-specific asymmetry factor derived from validation-set false-positive and false-negative rates; the authors prove it retains the weighted-exchangeability marginal coverage guarantee per class and show on BraTS 2020 and a covariate-shifted synthetic multi-organ CT benchmark that 95% Clopper–Pearson intervals for observed coverage contain the nominal 90% level for every class while interval width shrinks 8–14% versus symmetric weighted conformal prediction.