Graph-based anomaly detection (GBAD) approaches are among the most popular
techniques used to analyze connectivity patterns in communication networks and identify …

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 …

This book is about making machine learning models and their decisions interpretable. After
exploring the concepts of interpretability, you will learn about simple, interpretable models …

Understanding the inner workings of complex machine learning models is a long-standing
problem and most recent research has focused on local interpretability. To assess the role of …

Currently, most real-world time series datasets are multivariate and are rich in dynamical
information of the underlying system. Such datasets are attracting much attention; therefore …

In many supervised learning applications, understanding and visualizing the effects of the
predictor variables on the predicted response is of paramount importance. A shortcoming of …

Shapley values underlie one of the most popular model-agnostic methods within
explainable artificial intelligence. These values are designed to attribute the difference …

Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for
creating real-world intelligent systems. It provides a comprehensive approach with concepts …

We consider the problem of predicting a response variable from a set of covariates on a data
set that differs in distribution from the training data. Causal parameters are optimal in terms …

We propose privacy-preserving protocols for computing linear regression models, in the
setting where the training dataset is vertically distributed among several parties. Our main …