Bøker i Foundations and Trends (R) in Machine Learning-serien

Tensor Regression is the first thorough overview of the fundamentals, motivations, popular algorithms, strategies for efficient implementation, related applications, available datasets, and software resources for tensor-based regression analysis.

Minimum-Distortion Embedding describes the theory behind and practical use of a cutting-edge artificial intelligence technique. Accompanied by an open-source software package, PyMDE, it illustrates applying these AI techniques in areas such as images, co- networks, demographics, genetics, and biology.

The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. This book describes the latest state-of-the art.

Provides a review of existing graph kernels, their applications, software plus data resources, and an empirical comparison of state-of-the-art graph kernels. The book focuses on the theoretical description of common graph kernels, and on a large-scale empirical evaluation of graph kernels.

Presents an overview of the main theoretical insights that support the practical effectiveness of OT before explaining how to turn these insights into fast computational schemes. This book will be a valuable reference for researchers and students wishing to get a thorough understanding of computational optimal transport.

Reviews and extends some important results in random matrix theory in the specific context of real random Wishart matrices. To overcome the complexity of the subject matter, the authors use a lecture note style to make the material accessible to a wide audience. This results in a comprehensive and self-contained introduction.

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.

Presents an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient descent.