Contents
1 Introduction  
2 The Learning with Reject Problem Setting  
2.1 Models with a Reject Option  
2.2 Types of Rejection  
3 Evaluating Models  
3.1 Evaluating Models with a Fixed Rejection Rate  
3.2 Evaluating the Model Performance/Rejection Trade-off  
4 Separated Rejector  
5 Dependent Rejector  
6 Integrated Rejector  
7 Combining Multiple Rejectors  
8 Applications of Machine Learning Models with Rejection  
9 Link to Other Research Areas  
9.1 Uncertainty Quantification  
9.2 Anomaly Detection  
9.3 Active Learning  
9.4 Class-incremental / Incomplete Learning  
9.5 Delegating Classifiers  
9.6 Meta-learning  
10 Conclusions and Perspectives  
10.1 Research Questions Revisited  
10.2 Future Directions