Artificial intelligence (AI) algorithms based on neural networks have been designed for
decades with the goal of maximising some measure of accuracy. This has led to two …

Conformal prediction (CP) stands out as a robust framework for uncertainty quantification,
which is crucial for ensuring the reliability of predictions. However, common CP methods …

Conditional validity and length efficiency are two crucial aspects of conformal prediction
(CP). Achieving conditional validity ensures accurate uncertainty quantification for data …

Conformal prediction has shown impressive capacity in constructing statistically rigorous
prediction sets for machine learning models with exchangeable data samples. The siloed …