Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming. (arXiv:2109.11410v2 [cs.LG] UPDATED)

A critical bottleneck in supervised machine learning is the need for large
amounts of labeled data which is expensive and time consuming to obtain.
However, it has been shown that a small amount of labeled data, while
insufficient to re-train a model, can be effectively used to generate
human-interpretable labeling functions (LFs). These LFs, in turn, have been
used to generate a large amount of additional noisy labeled data, in a paradigm
that is now commonly referred to as data …

arxiv data labeling learning programming semi-supervised