Conformal prediction (CP) is a wrapper around traditional machine learning models, giving coverage guarantees under the sole assumption of exchangeability; in classification …
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 Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified …
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 …
A Bellotti - arXiv preprint arXiv:2105.11255, 2021 - arxiv.org
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 …
S Feldman, Y Romano - arXiv preprint arXiv:2406.05405, 2024 - arxiv.org
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 …
Conformal inference has played a pivotal role in providing uncertainty quantification for black-box ML prediction algorithms with finite sample guarantees. Traditionally, conformal …
G Cherubin - Conformal and Probabilistic Prediction with …, 2023 - proceedings.mlr.press
Conformal Prediction (CP) offers a shift on the traditional supervised classification paradigm. Whereas in supervised learning one generally aims to optimize the error of a classifier at …