Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

This chapter reviews the recent developments at the intersection of personalization and AI in
marketing and related fields. We provide a formal definition of personalized policy and …

Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

This monograph aims at providing an introduction to key concepts, algorithms, and
theoretical results in machine learning. The treatment concentrates on probabilistic models …

This self-contained introduction to machine learning, designed from the start with engineers
in mind, will equip students with everything they need to start applying machine learning …

Coherent uncertainty quantification is a key strength of Bayesian methods. But modern
algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior …

Computer simulations are used to model of complex physical systems. Often, these models
represent the solutions (or at least approximations) to partial differential equations that are …

Efficiently aggregating data from different sources is a challenging problem, particularly
when samples from each source are distributed differently. These differences can be …

When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo
(MCMC) algorithms have difficulty moving between modes, and default variational or mode …