Abstract Spaces with locally varying scale of measurement, like multidimensional structures
with differently scaled dimensions, are pretty common in statistics and machine learning …

Self-training is a simple yet effective method within semi-supervised learning. Self-training's
rationale is to iteratively enhance training data by adding pseudo-labeled data. Its …

We provide a theoretical and computational investigation of the Gamma-Maximin method
with soft revision, which was recently proposed as a robust criterion for pseudo-label …

This habilitation thesis is cumulative and, therefore, is collecting and connecting research
that I (together with several co-authors) have conducted over the last few years. Thus, the …

Given the vast number of classifiers that have been (and continue to be) proposed, reliable
methods for comparing them are becoming increasingly important. The desire for reliability …

This paper proposes a model for incomplete games where the knowledge of the players is
represented by a Dempster-Shafer belief function. Beyond an extension of the classical …

We provide a theoretical and computational investigation of the Gamma-Maximin method
with soft revision, which was recently proposed as a robust criterion for pseudo-label …