KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching

arXiv:2603.26415v1 Announce Type: cross Abstract: Uncertainty quantification is essential for deploying machine learning models in high-stakes domains such as scientific discovery and healthcare. Conformal Prediction (CP) provides finite-sample coverage guarantees under exchangeability, an assumption often violated in practice due to distribution shift. Under covariate shift, restoring validity requires importance weighting, yet accurate density-ratio estimation becomes unstable when training an