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

Recent advances in Transformer-based large language models (LLMs) have led to
significant performance improvements across many tasks. These gains come with a drastic …

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 …

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

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 …

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 …

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical
problem of estimating a bounded mean. Our approach generalizes and improves on the …