On the Complexity of Learning from Label Proportions
This addresses foundational complexity questions for LLP learning, applicable to settings like election prediction, but is incremental in characterizing its relationship to PAC learning.
The paper tackles the problem of learning from label proportions (LLP), where only label proportions are given instead of individual labels, and shows that for finite VC classes, efficient LLP learning is a strict subset of efficient PAC learning under standard complexity assumptions, with some classes having learnability independent of ZFC axioms.
In the problem of learning with label proportions, which we call LLP learning, the training data is unlabeled, and only the proportions of examples receiving each label are given. The goal is to learn a hypothesis that predicts the proportions of labels on the distribution underlying the sample. This model of learning is applicable to a wide variety of settings, including predicting the number of votes for candidates in political elections from polls. In this paper, we formally define this class and resolve foundational questions regarding the computational complexity of LLP and characterize its relationship to PAC learning. Among our results, we show, perhaps surprisingly, that for finite VC classes what can be efficiently LLP learned is a strict subset of what can be leaned efficiently in PAC, under standard complexity assumptions. We also show that there exist classes of functions whose learnability in LLP is independent of ZFC, the standard set theoretic axioms. This implies that LLP learning cannot be easily characterized (like PAC by VC dimension).