Sample-Efficient Generative Conformal Prediction
The paper introduces CASA, an adaptive sample allocation method for generative conformal prediction. Instead of using a fixed number of samples per input, CASA intelligently distributes a limited sampling budget across inputs. This adaptation minimizes the expected size of the prediction sets while maintaining coverage and adhering to the total sample budget.