Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision. (arXiv:2210.02724v1 [cs.LG])

Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to
synthesize training labels efficiently. The core component of PWS is the label
model, which infers true labels by aggregating the outputs of multiple noisy
supervision sources abstracted as labeling functions (LFs). Existing
statistical label models typically rely only on the outputs of LF, ignoring the
instance features when modeling the underlying generative process. In this
paper, we attempt to incorporate the instance features into a statistical label
model via …

aggregation arxiv features programmatic