The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

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

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

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 …

Vladimir Vovk Alexander Gammerman Glenn Shafer Second Edition Page 1 Vladimir Vovk
Alexander Gammerman Glenn Shafer Algorithmic Learning in a Random World Second …

Machine learning–based design has gained traction in the sciences, most notably in the
design of small molecules, materials, and proteins, with societal applications ranging from …

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 …

Clinical AI tools must convey predictive uncertainty for each individual patient | Nature Medicine
Skip to main content Thank you for visiting nature.com. You are using a browser version with …

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 …