Selective Prediction from Agreement: A Lipschitz-Consistent Version Space Approach

ArXi:2605.02611v1 Announce Type: new We consider selective classification with abstention in the fixed-pool (or transductive) setting, where the unlabeled pool is given beforehand and only a subset of points can be queried for labels. Our main insight is to view selective prediction through agreement: given queried labels and Lipschitz margin constraints in an embedding space, the version space of Lipschitz-consistent classification heads is well defined.