# 2 Non-robust prediction sets -------------------------------------------------
## 2.1 work with regular calibration for uncontaminted data
## 2.2 typically fail to provide the right coverage under data contamination
## 2.3 in the worst case could have severe coverage failure
# 3 Naive coverage expansion robust prediction sets ----------------------------
## 3.1 provides the right coverage under contamination
## 3.2 but can be arbitrarily bad in terms of size in the worst case
# 4 Projection (on true class) + expansion robust prediction sets --------------
## 4.1 also provides the right coverage under contamination
## 4.2 in addition, have better sizes compared to naive expansion sets
## 4.3 but projection step is computationally infeasible in general
# 5 Practical projection via outlier removal + coverage expansion --------------
## 5.1 provides the right coverage under structured contamination
## 5.2 and have good sizes compared to true projection + expansion sets
## 5.3 different outlier removal depending on different structural assumptions
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