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

The increasing power of computer technology does not dispense with the need to extract
meaningful information out of data sets of ever growing size, and indeed typically …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Identifying causal relationships and quantifying their strength from observational time series
data are key problems in disciplines dealing with complex dynamical systems such as the …

This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …

We describe two modifications that parallelize and reorganize caching in the well-known
Greedy Equivalence Search algorithm for discovering directed acyclic graphs on random …

Real world entities work in concert as a system and not in isolation. Understanding the
associations between these entities from their digital signatures can provide novel system …

The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

We present an algorithmic framework for learning local causal structure around target
variables of interest in the form of direct causes/effects and Markov blankets applicable to …

As artificial intelligence is being increasingly used for high-stakes applications, it is
becoming more and more important that the models used be interpretable. Bayesian …