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

KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching explores A conformal prediction framework using Kernel Mean Matching to provide reliable uncertainty quantification for machine learning models facing covariate shift, with code available.. Commercial viability score: 7/10 in Uncertainty Quantification.