2022
Firebolt: Weak Supervision Under Weaker Assumptions
Zhaobin Kuang, Chidubem G. Arachie, Bangyong Liang, Pradyumna Narayana, Giulia Desalvo, Michael S. Quinn +3
AISTATS 2022poster
Modern machine learning demands a large amount of training data. Weak supervision is a promising approach to meet this demand. It aggregates multiple labeling functions (LFs)–noisy, user-provided labeling heuristics—to rapidly and cheaply curate probabilistic labels for large-scale unlabeled data. H…