Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention

arXiv:2310.14770v2 Announce Type: replace
Abstract: Learning with abstention is a key scenario where the learner can abstain from making a prediction at some cost. In this paper, we analyze the score-based formulation of learning with abstention in the multi-class classification setting. We introduce new families of surrogate losses for the abstention loss function, which include the state-of-the-art surrogate losses in the single-stage setting and a novel family of loss functions in the two-stage setting. We prove strong non-asymptotic and hypothesis …

abstract algorithms analyze arxiv class classification cost cs.lg families functions key loss losses making paper prediction stat.ml type