Conformal prediction (CP) has been a popular method for uncertainty quantification
because it is distribution-free, model-agnostic, and theoretically sound. For forecasting …

This paper introduces a novel uncertainty quantification framework for regression models
where the response takes values in a separable metric space, and the predictors are in a …

Data following an interval structure are increasingly prevalent in many scientific applications.
In medicine, clinical events are often monitored between two clinical visits, making the exact …

In a supervised classification split conformal/inductive framework with $ K $ classes, a
calibration sample of $ n $ labeled examples is observed for inference on the label of a new …