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 …

Thanks to information explosion, data for the objects of interest can be collected from
increasingly more sources. However, for the same object, there usually exist conflicts among …

The Big Data era is upon us: data is being generated, collected and analyzed at an
unprecedented scale, and data-driven decision making is sweeping through all aspects of …

When a program fails to process an input, it need not be the program code that is at fault. It
can also be that the input data is faulty, for instance as result of data corruption. To get the …

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 …

The quality of web sources has been traditionally evaluated using exogenous signals such
as the hyperlink structure of the graph. We propose a new approach that relies on …

A fundamental problem in data fusion is to determine the veracity of multi-source data in
order to resolve conflicts. While previous work in truth discovery has proved to be useful in …

The task of {\em data fusion} is to identify the true values of data items (eg, the true date of
birth for {\em Tom Cruise}) among multiple observed values drawn from different sources …

A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform
for a large number of questions to be answered by Internet workers. Such systems have …