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 …

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 …

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

We propose a robust method for constructing conditionally valid prediction intervals based
on models for conditional distributions such as quantile and distribution regression. Our …

Accurate probabilistic predictions are essential for optimal decision making. While neural
network miscalibration has been studied primarily in classification, we investigate this in the …

The prediction accuracy of machine learning methods is steadily increasing, but the
calibration of their uncertainty predictions poses a significant challenge. Numerous works …

How can we quantify uncertainty if our favorite computational tool---be it a numerical,
statistical, or machine learning approach, or just any computer model---provides single …

Accurate uncertainty estimates are important in sequential model-based decision-making
tasks such as Bayesian optimization. However, these estimates can be imperfect if the data …

The continuous ranked probability score (crps) is the most commonly used scoring rule in
the evaluation of probabilistic forecasts for real-valued outcomes. To assess and rank …