Bøger i Foundations and Trends (R) in Machine Learning serien

A Markov Decision Process (MDP) is a natural framework for formulating sequential decision-making problems under uncertainty. In recent years, researchers have greatly advanced algorithms for learning and acting in MDPs. This book reviews such algorithms.

Offers an invitation to the field of matrix concentration inequalities. The book begins with some history of random matrix theory; describes a flexible model for random matrices that is suitable for many problems; and discusses the most important matrix concentration results.

Discusses models and methods for Bayesian inference in the simple single-step Bandit model. The book then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model.

Provides an overview of the historical development of statistical network modelling and then introduces a number of examples that have been studied in the network literature. Subsequent discussions focus on a number of prominent static and dynamic network models and their interconnections.

Provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. The book demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.

Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential families, this book develops general variational representations of the problems of computing likelihoods, marginal probabilities and most probable configurations.

Discusses the motivations for and principles of learning algorithms for deep architectures. By analysing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area.

Provides a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, the authors of this book relate them to each other and discuss their different implications.

Provides an overview of online learning. The aim is to provide the reader with a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms.

Provides a tutorial overview of several foundational methods for dimension reduction. The authors divide the methods into projective methods and methods that model the manifold on which the data lies.

Provides a comprehensible introduction to determinantal point processes (DPPs), focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and shows how DPPs can be applied to real-world applications.

Randomized algorithms for very large matrix problems have received much attention in recent years. Much of this work was motivated by problems in large-scale data analysis, largely since matrices are popular structures with which to model data drawn from a wide range of application domains. This book provides a detailed overview of this work.

Explores different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and regularization methods. The book is aimed at researchers interested in the theory and application of kernels for vector-valued functions.

Provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields.