Conformal prediction: A unified review of theory and new challenges Page 1 Bernoulli 29(1),
2023, 1–23 https://doi.org/10.3150/21-BEJ1447 Conformal prediction: A unified review of …

Convolutional image classifiers can achieve high predictive accuracy, but quantifying their
uncertainty remains an unresolved challenge, hindering their deployment in consequential …

Abstract Conformal inference, cross-validation+, and the jackknife+ are hold-out methods
that can be combined with virtually any machine learning algorithm to construct prediction …

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 …

When deploying machine learning models in high-stakes robotics applications, the ability to
detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe …

In this paper, we develop an inferential method based on conformal prediction, which can
wrap around any survival prediction algorithm to produce calibrated, covariate-dependent …

Early stopping based on hold-out data is a popular regularization technique designed to
mitigate overfitting and increase the predictive accuracy of neural networks. Models trained …

Regional forecasting is crucial for a balanced energy delivery system and for achieving the
global transition to clean energy. However, regional wind forecasting is challenging due to …

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

This paper presents a conformal inference method for out-of-distribution testing that
leverages side information from labelled outliers, which are commonly underutilized or even …