Pairwise Feedback for Data Programming - Robotics Institute Carnegie Mellon University

Pairwise Feedback for Data Programming

Workshop Paper, NeurIPS '19 Workshop on Learning with Rich Experience (LIRE '19), December, 2019

Abstract

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 labeled datasets via the synthesis of noisy heuristics provides a promising avenue to address this problem. We propose to improve modeling of latent class variables in the programmatic creation of labeled datasets by incorporating pairwise feedback into the process. We discuss the ease with which such pairwise feedback can be obtained or generated in many application domains. Our experiments show that even a small number of sources of pairwise feedback can substantially improve the quality of the posterior estimate of the latent class variable.

BibTeX

@workshop{Boecking-2019-121780,
author = {Benedikt Boecking and Artur Dubrawski},
title = {Pairwise Feedback for Data Programming},
booktitle = {Proceedings of NeurIPS '19 Workshop on Learning with Rich Experience (LIRE '19)},
year = {2019},
month = {December},
}