Ngan T. H. Le

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

AlignCP is proposed, a framework that reconciles supervised few-shot adaptation with conformal uncertainty estimation under non-exchangeability and learns a reweighted calibration distribution that reduces the score-level discrepancy between the labeled support set and the unlabeled query set.