My latest research is on weakly supervised machine learning, in which the goal is to
train models without hand labeled data. With the advent of data-hungry representation
learning techniques like deep neural networks, curating labeled training data has
replaced feature engineering as the most expensive and time consuming task in machine
learning. Weak supervision aims to overcome this bottleneck. I also work on statistical
relational learning and information extraction.
- Our work on weakly supervised sequence tagging, e.g., named entity
recognition, is accepted to AAAI 2020!
- Snorkel is now in production at Google. Our
paper at SIGMOD 2019 has the
technical details, and is featured on the
Google AI Blog.
- Our paper on Snorkel was
selected as a "Best of VLDB 2018" paper!
I lead the BATS machine learning research group. In the tradition of groups like
DAGS, BATS stands for "Bach's Awesome Team
Master's and Undergrad Students
- Berkan Hiziroglu
- Esteban Safranchik
- Dylan Sam
- Yang Zhang
Snorkel is a framework for creating noisy
training labels for machine learning. It uses statistical methods to combine weak
supervision sources like heuristic rules and task-related data sets, i.e., distant
supervision, which are far less expensive to use than hand labeling data. With the
resulting estimated labels, users can train many kinds of state-of-the-art models.
Snorkel is used at numerous technology companies like Google, research labs, and
agencies like the FDA.
Probabilistic soft logic is a formalism for
building statistical models over relational data like knowledge bases and social
networks. PSL programs define hinge-loss MRFs, a type of probabilistic graphical
model that admits fast, convex optimization for MAP inference, which makes them
very scalable. Researchers around the world have used PSL for bioinformatics,
computational social science, natural language processing, information extraction,
and computer vision.
In spring semesters, I teach machine learning
In Fall 2018, I taught a seminar on
learning with limited labeled data (CSCI 2952-C).