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A Hilbert space embedding of a distribution--in short, a kernel mean embedding--has recently emerged as a powerful tool for machine learning and statistical inf

Provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentia

This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4

The book covers theoretical questions including the latest extension of the formalism, and computational issues and focuses on some of the more fruitful and pro

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, gen