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 (CP) enables machine learning models to output prediction sets with
guaranteed coverage rate, assuming exchangeable data. Unfortunately, the exchangeability …

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 …

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 (CP) is a distribution-free uncertainty estimation framework that
constructs prediction sets guaranteed to contain the true answer with a user-specified …

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 …

We develop a method to generate prediction sets with a guaranteed coverage rate that is
robust to corruptions in the training data, such as missing or noisy variables. Our approach …

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 …