Quality improvement methods are essential to gathering high-quality crowdsourced data,
both for research and industry applications. A popular and broadly applicable method is task …

Many important data management and analytics tasks cannot be completely addressed by
automated processes. Crowdsourcing is an effective way to harness human cognitive …

Labeling training data is increasingly the largest bottleneck in deploying machine learning
systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the …

Labeling training data is increasingly the largest bottleneck in deploying machine learning
systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the …

Large labeled training sets are the critical building blocks of supervised learning methods
and are key enablers of deep learning techniques. For some applications, creating labeled …

Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …

As deep learning models are applied to increasingly diverse problems, a key bottleneck is
gathering enough high-quality training labels tailored to each task. Users therefore turn to …

Curating labeled training data has become the primary bottleneck in machine learning.
Recent frameworks address this bottleneck with generative models to synthesize labels at …

Crowdsourcing refers to solving large problems by involving human workers that solve
component sub-problems or tasks. In data crowdsourcing, the problem involves data …

We study the problem of learning neural text classifiers without using any labeled data, but
only easy-to-provide rules as multiple weak supervision sources. This problem is …