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

• URL: http://arxiv.org/abs/2603.26415v1
• Date: Fri, 27 Mar 2026 13:44:42 GMT
• Title: KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching
• Abstract summary: Conformal Prediction (CP) provides finite-sample coverage guarantees under exchangeability.<n>We propose KMM-CP, a conformal prediction framework based on Kernel Mean Matching (KMM)<n> Experiments on molecular property prediction benchmarks with realistic distribution shifts show that KMM-CP reduces coverage gap by over 50% compared to existing approaches.
• Score: 19.67518257951341
• License: http://creativecommons.org/licenses/by/4.0/
• 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-overlap regimes. Experiments on molecular property prediction benchmarks with realistic distribution shifts show that KMM-CP reduces coverage gap by over 50% compared to existing approaches. The code is available at https://github.com/siddharthal/KMM-CP.

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