Machine Learning in Non-Stationary Environments

Though important in practice and conceptually intriguing, the topic of covariate shift adaptation has only recently begun to attract significant attention in machine learning. Building on their sample reweighting methods, the authors assay a core problem of robust empirical inference. This timely book should be recommended to researchers and practitioners in a range of disciplines.

Bernhard Schölkopf, Max Planck Institute for Intelligent Systems

In machine learning we often assume that the characteristics of the data used to design a system will remain the same once the system is deployed. When this assumption is violated, and it does happen often, a system's accuracy may suffer significantly. This book provides the first in-depth look at how one can prepare for and cope with a frequently occurring instance of the above problem (covariate shift) both from theoretical and practical perspectives.

Neil Rubens, University of Electro-Communications, Japan

Written by two active researchers in the area, this book provides a highly accessible and self-contained exposition to some of the most important and recent advancements for tackling the covariate-shift problem. Students, researchers, and practitioners in related fields will benefit greatly from its huge collection of algorithms, numerical examples, and real-life applications.

Lihong Li, Yahoo! Research

This book provides a clear and practical guide to the problem of learning when the training and test data are drawn from different distributions. Of particular value are the many worked examples, illustrating the operation of the described techniques on real-life problems, and demonstrating their strengths, limitations, and areas of application.

Arthur Gretton, Gatsby Computational Neuroscience Unit, CSML, University College London