Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

We study the problem of uncertainty quantification for time series prediction, with the goal of
providing easy-to-use algorithms with formal guarantees. The algorithms we present build …

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

Conformal prediction is a technique for constructing prediction intervals that attain valid
coverage in finite samples, without making distributional assumptions. Despite this appeal …

While improving prediction accuracy has been the focus of machine learning in recent years,
this alone does not suffice for reliable decision-making. Deploying learning systems in …

Deep neural networks (DNNs) enable innovative applications of machine learning like
image recognition, machine translation, or malware detection. However, deep learning is …

Image-to-image regression is an important learning task, used frequently in biological
imaging. Current algorithms, however, do not generally offer statistical guarantees that …

We extend conformal prediction to control the expected value of any monotone loss function.
The algorithm generalizes split conformal prediction together with its coverage guarantee …

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 …