Data quality is crucial for robust machine learning algorithms, with the recent interest in data-
centric AI emphasizing the importance of training data characterization. However, current …

Regional forecasting is crucial for a balanced energy delivery system and for achieving the
global transition to clean energy. However, regional wind forecasting is challenging due to …

Gaussian Process Regression (GPR) is a popular regression method, which unlike most
Machine Learning techniques, provides estimates of uncertainty for its predictions. These …

Abstract We propose\textbf {JAWS}, a series of wrapper methods for distribution-free
uncertainty quantification tasks under covariate shift, centered on the core method\textbf …

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 …

Operator learning has been increasingly adopted in scientific and engineering applications,
many of which require calibrated uncertainty quantification. Since the output of operator …

The recently released Python package crepes can be used to generate both conformal
regressors, which transform point predictions into prediction intervals for specified levels of …

Abstract While Artificial Intelligence and Machine Learning models are becoming
increasingly prevalent, it is essential to remember that they are not infallible or inherently …

Quantile regression forests (QRF) is a generalization of random forests for quantile
regression, which can also output probabilistic prediction for regression problems. QRF …

Being able to understand the logic behind predictions or recommendations on the instance
level is at the heart of trustworthy machine learning models. Inherently interpretable models …