Partition Learning Conformal Prediction: Improving Conditional Coverage through Data-Driven Feature Extraction

The core message of this paper is to propose a data-driven framework called Partition Learning Conformal Prediction (PLCP) that can improve the conditional validity of prediction sets by learning uncertainty-guided features from the calibration data.