264 citations · 300 across the 11 of their papers we have counts for
6 papers · 1 filter
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performanc…
Generative Modeling Helps Weak Supervision (and Vice Versa)
Benedikt Boecking, Nicholas Roberts, Willie Neiswanger +3
Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To…
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…
Pairwise Feedback for Data Programming
Benedikt Boecking, Artur Dubrawski
The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning. The programmatic creation of la…