The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and
mutual information, respectively, has recently become quite common in machine learning …

Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue.
Uncertainty quantification is required for many applications, and disentangled aleatoric and …

Machine Learning and Deep Learning have achieved an impressive standard today,
enabling us to answer questions that were inconceivable a few years ago. Besides these …

The representation and quantification of uncertainty has received increasing attention in
machine learning in the recent past. The formalism of credal sets provides an interesting …

Uncertainty quantification has received increasing attention in machine learning in the
recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been …

Adequate uncertainty representation and quantification have become imperative in various
scientific disciplines, especially in machine learning and artificial intelligence. As an …

The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

Applying a machine learning model for decision-making in the real world requires to
distinguish what the model knows from what it does not. A critical factor in assessing the …

Due to the steadily increasing relevance of machine learning for practical applications, many
of which are coming with safety requirements, the notion of uncertainty has received …

Uncertainty quantification of deep neural networks has become an active field of research
and plays a crucial role in various downstream tasks such as active learning. Recent …