Bücher der Reihe Foundations and Trends (R) in Machine Learning

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.

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.

Presents some new concentration inequalities for Feynman-Kac particle processes. The book analyses different types of stochastic particle models, including particle profile occupation measures, genealogical tree based evolution models, particle free energies, as well as backward Markov chain particle models.

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.

Presents optimization tools and techniques dedicated to sparsity-inducing penalties from a general perspective. The book covers proximal methods, block-coordinate descent, working-set and homotopy methods, and non-convex formulations and extensions, and provides a set of experiments to compare algorithms from a computational point of view.

Argues that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.

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.

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 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 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.

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.

Describes methods for automatically compressing Markov decision processes (MDPs) by learning a low-dimensional linear approximation defined by an orthogonal set of basis functions. A unique feature of the text is the use of Laplacian operators, whose matrix representations have non-positive off-diagonal elements and zero row sums.

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.