Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

Feature selection is a crucial preprocessing step in data analytics and machine learning.
Classical feature selection algorithms select features based on the correlations between …

Three classes of algorithms to learn the structure of Bayesian networks from data are
common in the literature: constraint-based algorithms, which use conditional independence …

Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …

We propose a causal interpretation of self-attention in the Transformer neural network
architecture. We interpret self-attention as a mechanism that estimates a structural equation …

This article presents our vision on the need of developing new managing technologies to
harness distributed “computing continuum” systems. These systems are concurrently …

OBJECTIVES: Bayesian networks (BNs) are rapidly becoming a leading technology in
applied Artificial Intelligence, with many applications in medicine. Both automated learning …

Multi-output inference tasks, such as multi-label classification, have become increasingly
important in recent years. A popular method for multi-label classification is classifier chains …

We present a constraint-based algorithm for learning causal structures from observational
time-series data, in the presence of latent confounders. We assume a discrete-time …

We present a sound and complete algorithm, called iterative causal discovery (ICD), for
recovering causal graphs in the presence of latent confounders and selection bias. ICD …