Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Abstract Explainability in Artificial Intelligence (AI) has been revived as a topic of active
research by the need of conveying safety and trust to users in the “how” and “why” of …

In this paper we introduce a novel, unified, open-source model interpretability library for
PyTorch [12]. The library contains generic implementations of a number of gradient and …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

Hands-on Machine Learning with R provides a practical and applied approach to learning
and developing intuition into today's most popular machine learning methods. This book …

Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

We propose an algorithm for inexpensive gradient-based hyperparameter optimization that
combines the implicit function theorem (IFT) with efficient inverse Hessian approximations …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Complex nonparametric models—like neural networks, random forests, and support vector
machines—are more common than ever in predictive analytics, especially when dealing …

Modern artificial intelligence (AI) applications require large quantities of training and test
data. This need creates critical challenges not only concerning the availability of such data …