1 Introduction  
2 Preliminary  
3 Distribution Shift Adaptation  
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