Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical-or machine-
learning algorithm for prediction. In this article, we introduce a corresponding new …

Digital journalism has faced a dramatic change and media companies are challenged to use
data science algorithms to be more competitive in a Big Data era. While this is a relatively …

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data
collaboration without revealing their private data to each other. Recently, vertical FL, where …

Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function f
which is only accessible via point evaluations. It is typically used in settings where f is …

The fields of machine learning and causal inference have developed many concepts, tools,
and theory that are potentially useful for each other. Through exploring the possibility of …

Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …

Although Shapley values are theoretically appealing for explaining black-box models, they
are costly to calculate and thus impractical in settings that involve large, high-dimensional …

Most real-world data are scattered across different companies or government organizations,
and cannot be easily integrated under data privacy and related regulations such as the …

Background Feature selection, as a preprocessing stage, is a challenging problem in
various sciences such as biology, engineering, computer science, and other fields. For this …