Learning Hyper Label Model for Programmatic Weak Supervision. (arXiv:2207.13545v2 [cs.LG] UPDATED)

To reduce the human annotation efforts, the programmatic weak supervision
(PWS) paradigm abstracts weak supervision sources as labeling functions (LFs)
and involves a label model to aggregate the output of multiple LFs to produce
training labels. Most existing label models require a parameter learning step
for each dataset. In this work, we present a hyper label model that (once
learned) infers the ground-truth labels for each dataset in a single forward
pass without dataset-specific parameter learning. The hyper label model …

arxiv programmatic