[2603.26415] KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching

Computer Science > Machine Learning arXiv:2603.26415 (cs) [Submitted on 27 Mar 2026] Title:KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching Authors:Siddhartha Laghuvarapu, Rohan Deb, Jimeng Sun View a PDF of the paper titled KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching, by Siddhartha Laghuvarapu and 2 other authors View PDF HTML (experimental) 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 and test distributions exhibit limited support overlap. We propose KMM-CP, a conformal prediction framework based on Kernel Mean Matching (KMM) for covariate-shift correction. We show that KMM directly controls the bias-variance components governing conformal coverage error by minimizing RKHS moment discrepancy under explicit weight constraints, and establish asymptotic coverage guarantees under mild conditions. We then introduce a selective extension that identifies regions of reliable support overlap and restricts conformal correction to this subset, further improving stability in low-ov...