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

28 - Data Programming: Creating Large Training Sets, Quickly

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28 - Data Programming: Creating Large Training Sets, Quickly

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