We propose a new inference framework called localized conformal prediction. It generalizes
the framework of conformal prediction by offering a single-test-sample adaptive construction …

We introduce Longitudinal Predictive Conformal Inference (LPCI), a novel distribution-free
conformal prediction algorithm for longitudinal data. Current conformal prediction …

Conformal prediction (CP) for regression can be challenging, especially when the output
distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed …

Conformal prediction methods are a tool for uncertainty quantification of a model's
prediction, providing a model-agnostic and distribution-free statistical wrapper that …

Conformal inference is a popular tool for constructing prediction intervals. We consider here
the scenario of post-selection/selective conformal inference, that is, prediction intervals are …

Conformal prediction is a machine learning methodology that produces valid prediction
regions. Ensembles of conformal predictors have been proposed to improve the …

Abstract Inductive Conformal Prediction (ICP) provides a practical and effective approach for
equipping deep learning models with uncertainty estimates in the form of set-valued …

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 proposes probabilistic conformal prediction (PCP), a predictive inference
algorithm that estimates a target variable by a discontinuous predictive set. Given inputs …

Coresets have been proven useful in accelerating the computation of inductive conformal
predictors (ICP) when the training data becomes large in size. This work shows that …