This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action ...

GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.

Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables.

This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years.

... approach. However, user preferences are undisclosed and different from user to user. The current developments in machine learning ... process can adapt to the user. Current hardware capabilities allow to process a large amount of data, and at ...

... Gaussian process to include the preferences imposed by the ML and CL constraints. Recently, Pei et al. [9] propose a discriminative clustering model that uses relative comparisons and, like our method, can also make use of unspecified ...

... Gaussian Process Latent Variable Model (GPLVM) [51– 54] was studied. It is a non-parametric technique that can ... metric can be computed by using either pairwise loss or triplet loss. For the pairwise loss, its related methods include ...

Metric Learning: A Review presents an overview of existing research in metric learning, including recent progress on scaling to high-dimensional feature spaces and to data sets with an extremely large number of data points.

This book constitutes the refereed proceedings of the First ECML PKDD Workshop, AALTD 2015, held in Porto, Portugal, in September 2016. The 11 full papers presented were carefully reviewed and selected from 22 submissions.

Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.