- Title Pages
- Series Foreword
- 1 When Training and Test Sets Are Different: Characterizing Learning Transfer
- Projection and Projectability
- 3 Binary Classification 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
- 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
- 11 Author Comments
- Notation and Symbols
- Dataset Shift in Machine Learning
- The MIT Press
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