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 predictors are an important class of algorithms that allow predictions to be made
with a user-defined confidence level. They are able to do this by outputting prediction sets …

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

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

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 …

We consider the problem of constructing distribution-free prediction sets with finite-sample
conditional guarantees. Prior work has shown that it is impossible to provide exact …

In this paper, we focus on the problem of conformal prediction with conditional guarantees.
Prior work has shown that it is impossible to construct nontrivial prediction sets with full …

Abstract Predictive UNcertainty Calibration and Conformalization (PUNCC) is an open-
source Python library integrating a collection of state-of-the-art Conformal Prediction (CP) …

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