Title: Conditional Flow Matching for Conformal Regression
Primary area: interpretability and explainable AI
Keywords: Conformal prediction, conformal regression, conditional flow matching

Abstract: This paper introduces Conditional flow Matching for conformal Regression (CMR), a novel framework that combines conditional flow matching with conformal prediction to generate reliable prediction intervals with guaranteed coverage. By learning the conditional distribution of the response variable through conditional normalizing flows and coupling it with a calibration procedure, CMR constructs prediction intervals that are both statistically valid and efficiently sized. Our approach trains a conditional flow model using a simulation-free training method, then employs a novel nonconformity score based on the shortest interval containing the true value among generated samples. Extensive experiments on simulated and real-world datasets demonstrate that CMR consistently produces narrower prediction intervals while maintaining the required coverage level and achieving superior tail coverage compared to state-of-the-art methods.