Many evolutionary artificial intelligence (AI) technologies have been applied to assist with
job scheduling in manufacturing. One of the main approaches is genetic algorithms (GAs) …

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

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 …

Abstract Conformal inference, cross-validation+, and the jackknife+ are hold-out methods
that can be combined with virtually any machine learning algorithm to construct prediction …

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 propose a model-free framework for sensitivity analysis of individual treatment effects
(ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the …

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

Score-based generative modeling, informally referred to as diffusion models, continue to
grow in popularity across several important domains and tasks. While they provide high …

In this paper, we develop an inferential method based on conformal prediction, which can
wrap around any survival prediction algorithm to produce calibrated, covariate-dependent …