Selective Classification Under Distribution Shifts

arXiv:2405.05160v1 Announce Type: new
Abstract: In selective classification (SC), a classifier abstains from making predictions that are likely to be wrong to avoid excessive errors. To deploy imperfect classifiers -- imperfect either due to intrinsic statistical noise of data or for robustness issue of the classifier or beyond -- in high-stakes scenarios, SC appears to be an attractive and necessary path to follow. Despite decades of research in SC, most previous SC methods still focus on the ideal statistical setting …

abstract arxiv beyond classification classifier classifiers cs.ai cs.cv cs.lg data deploy distribution errors intrinsic issue making noise predictions robustness statistical type