Supervised learning is an important branch of machine learning (ML), which requires a
complete annotation (labeling) of the involved training data. This assumption is relaxed in …

In this article we study the problem of learning from fuzzy labels (LFL), a form of weakly
supervised learning in which the supervision target is not precisely specified but is instead …

The problem of learning from imprecise data has recently attracted increasing attention, and
various methods to tackle this problem have been proposed. In this paper, we discuss and …

This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …

We propose a minimax regret empirical prior for inefficiencies in a stochastic frontier model
and for its other parameters. The class of priors over which we consider minimax regret is …

Various methods have been proposed to express and solve maximum likelihood problems
with incomplete data. In some of these approaches, the idea is that incompleteness makes …

This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …

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 …

This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …

The representation, quantification and proper management of uncertainty is one of the
central problems in Artificial Intelligence, and particularly so in Machine Learning, in which …