Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …

Federated learning aims to learn a global model that performs well on client devices with
limited cross-client communication. Personalized federated learning (PFL) further extends …

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or
quasars from multiband photometry. Surveys in narrow-band filters allow for greater …

Weakly supervised machine learning algorithms are able to learn from ambiguous samples
or labels, eg, multi-instance learning or partial-label learning. However, in some real-world …

Li-ion batteries store electrical energy by electrochemically reducing Li ions from a liquid
electrolyte in a graphitic electrode. During these reactions, electrolytic species in contact …

Operational Earth observation missions, like the Sentinel-3 (S3) satellites, aim to provide
imagery for long-term environmental assessment to monitor and analyze vegetation …

Abstract Skew-Gaussian Processes (SkewGPs) extend the multivariate Unified Skew-
Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs …

Around one-third of the point-like sources in the Fermi-LAT catalogues remain as
unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association …

The Gaussian process (GP) algorithm is considered as a powerful nonparametric-learning
approach, which can provide uncertainty measurements on the predictions. The standard …

A missing value indicates that a particular attribute of an instance of a learning problem is
not recorded. They are very common in many real-life datasets. In spite of this, however …