1 Introduction  
2 Preliminary  
2.1 Density Ratio Estimation  
3 Distribution Shift Adaptation  
3.1 Covariate Shift  
3.2 Target Shift  
3.3 Sample Selection Bias  
3.4 Subpopulation Shift  
3.5 Feedback Shift  
3.6 Domain Adaptation  
3.7 Distributionally Robust Optimization  
4 Active Learning  
5 Model Calibration  
6 Positive-Unlabeled (PU) Learning  
7 Label Noise Correction  
8 Subgroup Fairness  
9 Importance Weighting and Deep Learning  
10 Conclusion and Perspective