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

We introduce an extension of conformal predictions (CP) based on a combination of split-CP
and the Jackknife+ procedure that enables tuning score functions to calibration data and …

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

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

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 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 …

We develop a new method for creating prediction sets that combines the flexibility of
conformal methods with an estimate of the conditional distribution $ P_ {Y\mid X} $. Most …

Training-conditional coverage guarantees in conformal prediction concern the concentration
of the error distribution, conditional on the training data, below some nominal level. The …

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