- Title Pages
- Series Foreword
1When Training and Test Sets Are Different: Characterizing Learning Transfer
- Projection and Projectability
3Binary Classification under Sample Selection Bias
4On Bayesian Transduction: Implications for the Covariate Shift Problem
5On the Training/Test Distributions Gap: A Data Representation Learning Framework
6Geometry of Covariate Shift with Applications to Active Learning
7A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift
8Covariate Shift by Kernel Mean Matching
9Discriminative Learning under Covariate Shift with a Single Optimization Problem
10An Adversarial View of Covariate Shift and a Minimax Approach
- Notation and Symbols
- Dataset Shift in Machine Learning
- The MIT Press
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