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Covers the Thompson sampling algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, sh...Savoir plus
Reviews a branch of Monte Carlo methods that are based on the forward-backward idea, and that are referred to as backward simulators. In recent years,...Savoir plus
Identifies unifying principles, patterns, and intuitions for scaling Bayesian inference. This book examines how these techniques can be scaled up to l...Savoir plus
Reinforcement learning (RL) is one of the foundational pillars of artificial intelligence and machine learning. An important consideration in any opti...Savoir plus
Offers an invitation to the field of matrix concentration inequalities. The book begins with some history of random matrix theory; describes a flexibl...Savoir plus
Provides a textbook like treatment of multi-armed bandits. The work on multi-armed bandits can be partitioned into a dozen or so directions. Each chap...Savoir plus
Discusses the motivations for and principles of learning algorithms for deep architectures. By analysing and comparing recent results with different l...Savoir plus
Discusses models and methods for Bayesian inference in the simple single-step Bandit model. The book then reviews the extensive recent literature on B...Savoir plus
Examines the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis an...Savoir plus
Working with exponential family representations, and exploiting the conjugate duality between the cumulant function and the entropy for exponential fa...Savoir plus
Provides a comprehensible introduction to determinantal point processes (DPPs), focusing on the intuitions, algorithms, and extensions that are most r...Savoir plus
Surveys recent progress in using spectral methods, including matrix and tensor decomposition techniques, to learn many popular latent variable models....Savoir plus
Explores different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between proba...Savoir plus
Provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge ...Savoir plus
Describes methods for automatically compressing Markov decision processes (MDPs) by learning a low-dimensional linear approximation defined by an orth...Savoir plus
Provides a starting point for understanding deep reinforcement learning. Although written at a research level it provides a comprehensive and accessib...Savoir plus
Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. In this volume, the authors e...Savoir plus
Presents the theory of submodular functions in a self-contained way from a convex analysis perspective, presenting tight links between certain polyhed...Savoir plus
Variational autoencoders are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent s...Savoir plus
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 underscor...Savoir plus
Argues that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale proble...Savoir plus
Presents an overview of existing research in this topic, including recent progress on scaling to high-dimensional feature spaces and to data sets with...Savoir plus
Many modern methods for prediction leverage nearest neighbour search to find past training examples most similar to a test example, an idea that dates...Savoir plus
Provides a simple and clear description of explicit-duration modelling by categorizing the different approaches into three main groups, which differ i...Savoir plus