Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Alex Ratner

Alex Ratner
Graduate Student, Stanford University

Alex Ratner is a third-year PhD student at the Stanford InfoLab working under Chris Re. Alex works on new machine learning paradigms for settings where limited or no hand-labeled training data is available, motivated in particular by information extraction problems in domains like genomics, clinical diagnostics, and political science. He coleads the development of the Snorkel framework for lightweight information extraction.

Sessions

Artificial Intelligence, Machine Learning & Data Science
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Hardcore Data Science
Alex Ratner (Stanford University)
Average rating: ****.
(4.40, 5 ratings)
As data-hungry algorithms become the norm in machine learning, the bottleneck is now acquiring labeled training data. Alex Ratner explores data programming, a paradigm for the programmatic creation of training sets in which users express weak supervision strategies or domain heuristics as simple scripts called labeling functions, which are then automatically denoised. Read more.