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 …

Conformal prediction beyond exchangeability Page 1 The Annals of Statistics 2023, Vol. 51, No.
2, 816–845 https://doi.org/10.1214/23-AOS2276 © Institute of Mathematical Statistics, 2023 …

Predictive inference with the jackknife+ Page 1 The Annals of Statistics 2021, Vol. 49, No. 1,
486–507 https://doi.org/10.1214/20-AOS1965 © Institute of Mathematical Statistics, 2021 …

We study the problem of uncertainty quantification via prediction sets, in an online setting
where the data distribution may vary arbitrarily over time. Recent work develops online …

Testing for outliers with conformal p-values Page 1 The Annals of Statistics 2023, Vol. 51, No.
1, 149–178 https://doi.org/10.1214/22-AOS2244 © Institute of Mathematical Statistics, 2023 …

Conformal prediction (CP) is a framework to quantify uncertainty of machine learning
classifiers including deep neural networks. Given a testing example and a trained classifier …

Dealing with uncertain data in statistical estimation problems or in machine learning is not
really a new issue. However, such uncertainty has so far mostly been modelled either as …

Machine learning for malware classification shows encouraging results, but real
deployments suffer from performance degradation as malware authors adapt their …

Selective classification allows models to abstain from making predictions (eg, say" I don't
know") when in doubt in order to obtain better effective accuracy. While typical selective …

Deep learning models achieve high predictive accuracy across a broad spectrum of tasks,
but rigorously quantifying their predictive uncertainty remains challenging. Usable estimates …