Abstract Out-of-distribution (OOD) detection has been extensively studied in order to
successfully deploy neural networks, in particular, for safety-critical applications. Moreover …

Demand forecasting is a crucial component of demand management. While shortening the
forecasting horizon allows for more recent data and less uncertainty, this frequently means …

Analyzing classification model performance is a crucial task for machine learning
practitioners. While practitioners often use count-based metrics derived from confusion …

Data-driven predictive maintenance is typically based on collected data from multiple
sensors or industrial systems over a period of time, where historical and real-time data are …

Quality control is a crucial activity performed by manufacturing enterprises to ensure that
their products meet quality standards and avoid potential damage to the brand's reputation …

Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or
confidence scores, are ubiquitous in science and society, and methods for evaluating and …

In this paper, we formalize the problem of learning coherent collections of decision models,
which we call decision catalogues, and illustrate it for the case where models are scoring …

In supervised learning problems, it is common to have a lot of unlabeled data, but little
labeled data. It is then desirable to leverage the unlabeled data to improve the learning …

Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated
class probabilities. In high-risk applications like healthcare, practitioners require $\textit {fully …

Effective management of chronic diseases and cancer can greatly benefit from disease-
specific biomarkers that enable informative screening and timely diagnosis. IgG N-glycans …