We demonstrate that numerous machine learning algorithms are specific instances of one
single paradigm: reciprocal learning. These instances range from active learning over multi …

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 …

Although being a crucial question for the development of machine learning algorithms, there
is still no consensus on how to compare classifiers over multiple data sets with respect to …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies of depth functions in linear and metric …

Learning human objectives from preference feedback has significantly advanced
reinforcement learning (RL) in domains with hard-to-formalize objectives. Traditional …

We propose a framework for descriptively analyzing sets of partial orders based on the
concept of depth functions. Despite intensive studies in linear and metric spaces, there is …

In this paper, we develop statistical models for partial orders where the partially ordered
character cannot be interpreted as stemming from the non-observation of data. After …

The quality of consequences in a decision making problem under (severe) uncertainty must
often be compared among different targets (goals, objectives) simultaneously. In addition …

The aim of the study was to use a multi-criteria decisionmaking method to make a rational
choice for a new location for the distribution centre of agricultural products in the …