Conformal Prediction with Macro-Coverage Guarantees
Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others. In the classification setting, class-conditional coverage requires that the prediction set (i.e., the set of candidate labels for a new test point) must achieve the target accuracy level within each class, which may be challenging to satisfy when many classes are rare and have few calibration points. At the other extreme, marginal coverage requires only that coverage holds on average over the distribution of all classes, which can lead to low-probability labels being essentially ignored. To find a middle ground, recent work has introduced macro-coverage, defined as ...
| Authors | Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel Barber |
|---|---|
| Primary category | stat.ME |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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