Motivation and Focus
The notion of uncertainty is of major importance in machine learning and constitutes a key element of modern machine learning methodology. In recent years, it has gained in importance due to the increasing relevance of machine learning for practical applications, many of which are coming with safety requirements. In this regard, new problems and challenges have been identified by machine learning scholars, which call for new methodological developments. Indeed, while uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions, recent research has gone beyond traditional approaches and also leverages more general formalisms and uncertainty calculi. For example, a distinction between different sources and types of uncertainty, such as aleatoric and epistemic uncertainty, turns out to be useful in many machine learning applications. The workshop will pay specific attention to recent developments of this kind.

Aim and Scope
The goal of this workshop is to bring together researchers interested in the topic of uncertainty in machine learning. It is meant to provide a place for the discussion of the most recent developments in the modeling, processing, and quantification of uncertainty in machine learning problems, and the exploration of new research directions in this field.

Topics of Interest
The scope of the workshop covers, but is not limited to, the following topics:

adversarial examples

aleatoric and epistemic uncertainty

Bayesian methods

belief functions

calibration

classification with reject option

conformal prediction

credal classifiers

(uncertainty in) deep learning and neural networks

ensemble methods

imprecise probability

likelihood and fiducial inference

hypothesis testing

model selection and misspecification

multi-armed bandits

noisy data and outliers

online learning

out-of-sample prediction

out-of-distribution detection

uncertainty in optimization

performance evaluation

prediction intervals

probabilistic methods

reliable prediction

set-valued prediction

uncertainty quantification

weakly supervised learning