Uncertainty is critical to reliable decision-making with machine learning. Conformal
prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to …

Conformal prediction (CP) is an attractive theoretical framework for estimating the
uncertainties of any predictive algorithms as its methodology is general and systematic with …

Inductive Conformal Prediction (ICP) is a set of distribution-free and model agnostic
algorithms devised to predict with a user-defined confidence with coverage guarantee …

Conformal prediction is a theoretically grounded framework for constructing predictive
intervals. We study conformal prediction with missing values in the covariates–a setting that …

Inductive conformal predictors have been designed to overcome the computational
inefficiency exhibited by conformal predictors for many underlying prediction algorithms …

This thesis develops new methods and associated coverage guarantees for conformal
inference. We begin by discussing applications of the conformal framework to the online …

Modern image classifiers are very accurate, but the predictions come without uncertainty
estimates. Conformal predictors provide uncertainty estimates by computing a set of classes …

Predictive models make mistakes. Hence, there is a need to quantify the uncertainty
associated with their predictions. Conformal inference has emerged as a powerful tool to …

This special issue of Machine Learning is devoted to conformal prediction, an emerging
technique in machine learning that focuses on designing prediction algorithms that are …

We study the efficient estimation of predictive confidence intervals for black-box predictors
when the common data exchangeability (eg, iid) assumption is violated due to potentially …