Contents -- Series Foreword -- Preface -- I - Introduction to Dataset Shift -- 1 - When Training and Test Sets Are Di erent: Characterizing Learning Transfer -- 2 - Projection and Projectability -- II - Theoretical Views on Dataset and Covariate Shift -- 3 - Binary Classi cation under Sample Selection Bias -- 4 - On Bayesian Transduction: Implications for the Covariate Shift Problem -- 5 - On the Training/Test Distributions Gap: A Data Representation Learning Framework -- III - Algorithms for Covariate Shift -- 6 - Geometry of Covariate Shift with Applications to Active Learning -- 7 - A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift -- 8 - Covariate Shift by Kernel Mean Matching -- 9 - Discriminative Learning under Covariate Shift with a Single Optimization Problem -- 10 - An Adversarial View of Covariate Shift and a Minimax Approach -- IV - Discussion -- 11 - Author Comments -- References -- Notation and Symbols -- Contributors -- Index.