Presenting Snuba, a system to automatically generate heuristics using a small labeled dataset to assign training labels to a large, unlabeled dataset in the weak supervision setting.

This is first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting

Proposing Slice-based Learning, a new programming model in which the slicing function (SF), a programmer abstraction, is used to specify additional model capacity for each slice.

This paper introduces a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few labeled examples.

Proposing Osprey, a weak-supervision system suited for highly imbalanced data, built on top of the Snorkel framework.

Proposing Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data.

Showcasing state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data.

This work focuses on a robust PCA-based algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real world tasks.