Data Programming: Creating Large Training Sets, Quickly

Recruit Communications, EngineerKotaro Tanahashi

Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré Stanford University

NIPS 2016, reading meet up

[https://www.youtube.com/watch?v=iSQHelJ1xxU]

ML model requires lots of training dataProblem:

[https://www.youtube.com/watch?v=iSQHelJ1xxU]

💡Key Idea: using labeling function created by domain experts

Examples of Labeling Function

Independent Labeling Functions

λ is true λ is false λ gives no label

family of generative model

αi : probability labeling the object correctly βi : probability labeling an object

determine α, β by MAP estimation

Training of wfinal goal is training w in

optimal w is obtained by

f(x) : arbitrary feature mapping

noise-aware empirical risk

[https://www.youtube.com/watch?v=iSQHelJ1xxU]

Handling DependenciesIn some cases, the dependency among labeling functions is obvious like

considering the dependency can improve accuracy

fix: whenever λ2 labels, λ1 labels when λ1,λ2 disagree, λ2 is correct

reinforce: when λ1,λ2 typically agree

Generalization of Generative Model

λ is true λ is false λ gives no label

generalize

h is a factor function

α, β → θ

Represent Dependency by h

For a fixing dependency

whenever λj labels, λi labels

λj is true and λi is false

θ

Training procedure is same as before

Experimental Results

coverage: % of #label > 0overlap: % of #label > 1|S|: #generated label

Data Programing outperforms LSTM

[https://www.youtube.com/watch?v=iSQHelJ1xxU]

RCO tech-blog

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