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

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 (CP) is a method for constructing a prediction interval around the
output of a fitted model, whose validity does not rely on the model being correct--the CP …

In supervised learning, including regression and classification, conformal methods provide
prediction sets for the outcome/label with finite sample coverage for any machine learning …

Conformal inference has played a pivotal role in providing uncertainty quantification for
black-box ML prediction algorithms with finite sample guarantees. Traditionally, conformal …

In this work, we consider the problem of building distribution-free prediction intervals with
finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increasingly …

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

Conformal prediction is an assumption-lean approach to generating distribution-free
prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite …

We compare two recent methods that combine conformal inference with quantile regression
to produce locally adaptive and marginally valid prediction intervals under sample …

Conformal prediction, and split conformal prediction as a specific implementation, offer a
distribution-free approach to estimating prediction intervals with statistical guarantees …