The adoption of machine learning (ML) and, more specifically, deep learning (DL)
applications into all major areas of our lives is underway. The development of trustworthy AI …

Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks,
including abstained prediction, out-of-distribution detection, and aleatoric uncertainty …

The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

Calls for representation in artificial intelligence (AI) and machine learning (ML) are
widespread, with" representation" or" representativeness" generally understood to be both …

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has
made tremendous progress in solving multiple real-world problems such as disease …

Abstract Representation learning has significantly driven the field to develop pretrained
models that can act as a valuable starting point when transferring to new datasets. With the …

Contrastively trained encoders have recently been proven to invert the data-generating
process: they encode each input, eg, an image, into the true latent vector that generated the …

Credal sets are sets of probability distributions that are considered as candidates for an
imprecisely known ground-truth distribution. In machine learning, they have recently …

Multimodal large language models (MLLMs) have demonstrated promising results in a
variety of tasks that combine vision and language. As these models become more integral to …