Selecting informative conformal prediction sets with false coverage rate control
具有错误覆盖率控制的信息性共形预测集选择
Abstract In supervised learning, including regression and classification, conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider here the case where such prediction sets come after a selection process. The selection process requires that the selected prediction sets be ‘informative’ in a well-defined sense. We consider both the classification and regression settings where the analyst may…