Abstract Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …

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 develop methods for forming prediction sets in an online setting where the data
generating distribution is allowed to vary over time in an unknown fashion. Our framework …

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

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 …

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

Multi-class classification problem is among the most popular and well-studied statistical
frameworks. Modern multi-class datasets can be extremely ambiguous and single-output …

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 introduce a framework for calibrating machine learning models so that their predictions
satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work with any …

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 …