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The Label Complexity of Useful Class-Conditional Prediction Sets under Distribution Shift
Prediction sets can make deployed classifiers safer by returning several plausible labels when a single prediction is uncertain. Their value depends on classwise reliability: average coverage can meet its target while rare or difficult classes fail repeatedly. This concern is sharper after distribut
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