264 citations · 300 across the 11 of their papers we have counts for
3 papers · 1 filter
End-to-End Weak Supervision
Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski
Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collect…
Dependency Structure Misspecification in Multi-Source Weak Supervision Models
Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski
Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data. In DP, users encode domain knowledge into \emph{labeling functions} (LF), heuristi…
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Benedikt Boecking, Willie Neiswanger, Eric Xing +1
Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alterna…