Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

Artificial Intelligence (AI) is applied in almost every public sector because of its positive
impacts. However, AI's ethical aspects and trustworthiness constitute a significant uproar …

In this paper we investigate risk prediction of criminal re-offense among juvenile defendants
using general-purpose machine learning (ML) algorithms. We show that in our dataset …

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …

Homelessness service provision, a task of great societal relevance, requires solutions to
several urgent problems facing our humanity. Data science, that has recently emerged as a …

Ranking items is a vital component in almost every application dealing with selecting the
most suitable items among a pool of candidates. Yet, specific individuals or groups may be …

In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between
innovation and regulation has become an emerging focus of our society. Despite …

Tree ensembles are one of the most powerful methodologies in Machine Learning. In this
paper, we investigate how to make tree ensembles more flexible to incorporate by design …

While the field of algorithmic fairness has brought forth many ways to measure and improve
the fairness of machine learning models, these findings are still not widely used in practice …

Clustering is a strong computational approach that is used in many datadriven
bioinformatics studies. Clustering is very useful for evaluating unstructured and high …