28 - Data Programming: Creating Large Training Sets, Quickly

NIPS 2016 paper by Alexander Ratner and coauthors in Chris Ré's
group at Stanford, presented by Waleed. The paper presents a method
for generating labels for an unlabeled dataset by combining a
number of weak labelers. This changes the annotation effort from
looking at individual examples to constructing a large number of
noisy labeling heuristics, a task the authors call "data
programming". Then you learn a model that intelligently aggregates
information from the weak labelers to create a weighted
"supervised" training set. We talk about this method, how it works,
how it's related to ideas like co-training, and when you might want
to use it.
https://www.semanticscholar.org/paper/Data-Programming-Creating-Large-Training-Sets-Quic-Ratner-Sa/37acbbbcfe9d8eb89e5b01da28dac6d44c3903ee