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

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 …

Conformal prediction (CP) is a wrapper around traditional machine learning models, giving
coverage guarantees under the sole assumption of exchangeability; in classification …

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

Conformal prediction (CP) is a wrapper around traditional machine learning models, giving
coverage guarantees under the sole assumption of exchangeability; in classification …

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 …

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

Despite attractive theoretical guarantees and practical successes, Predictive Interval (PI)
given by Conformal Prediction (CP) may not reflect the uncertainty of a given model. This …

Conformal prediction is a new framework produc-ing region predictions with a guaranteed
error rate. Inductive conformal prediction (ICP) was designed to significantly reduce the …