Weighted Conformal Prediction
Overview
Weighted conformal prediction studies how to form prediction sets and intervals with reliable coverage when calibration and deployment data have different distributions. It reweights calibration scores using estimated density ratios, kernel matching, or local and temporal similarity, then applies weighted quantiles or conformal p-values. These methods are used for covariate and label shift, online and federated prediction, and settings such as molecular design, forecasting, and medical imaging. A central tension is that weighting can improve relevance to deployment data but unstable estimates, limited support overlap, and uncertain weights can weaken guarantees or make predictions wider; sharper conditional guarantees and efficient, robust calibration remain open challenges.
Latest papers
29 papers2 papers in the last four weeks, down 75% on the four weeks before. 0.0% of all new papers.