Stanford MLSys Seminar Episode 51: Fred Sala [Th, 1.35-2.30pm PT]

[Karan Goel]

Hi everyone,

We're back with the fifty-first episode of the MLSys Seminar on Thursday from 1.35-2.30pm PT. 

We'll be joined by Fred Sala, who will talk about efficient dataset construction. The format is a 30 minute talk followed by a 30 minute podcast-style discussion, where the live audience can ask questions.

Livestream Link: https://www.youtube.com/watch?v=XbnAYeSJ3EU

Guests: Fred Sala

Title: Efficiently Constructing Datasets for Diverse Datatypes

Abstract: Building large datasets for data-hungry models is a key challenge in modern machine learning. Weak supervision frameworks have become a popular way to bypass this bottleneck. These approaches synthesize multiple noisy but cheaply-acquired estimates of labels into a set of high-quality pseudolabels for downstream training. In this talk, I introduce a technique that fuses weak supervision with structured prediction, enabling WS techniques to be applied to extremely diverse types of data. This approach allows for labels that can be continuous, manifold-valued (including, for example, points in hyperbolic space), rankings, sequences, graphs, and more. I will discuss theoretical guarantees for this universal weak supervision technique, connecting the consistency of weak supervision estimators to low-distortion embeddings of metric spaces. I will show experimental results in a variety of problems, including learning to rank, geodesic regression, and semantic dependency parsing. Finally I will present and discuss future opportunities for automated dataset construction.
Bio: Frederic Sala is an Assistant Professor in the Computer Sciences Department at the University of Wisconsin-Madison and a research scientist at Snorkel AI. His research studies the foundations of data-driven systems, with a focus on machine learning systems. Previously, he was a postdoctoral researcher in the Stanford CS department. He received his Ph.D. in Electrical Engineering from UCLA.

See you all there!

Best,

Karan

[Karan Goel]

Reminder: we're starting in 10 minutes!