Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation

arXiv:2310.08056v4 Announce Type: replace
Abstract: Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework for this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in …

abstract aim arxiv belief bootstrapping cs.ai cs.lg data domains instance instances labels performance propagation test training type via