Stanford MLSys Seminar Episode 53: Cody Coleman [Th, 1.35-2.30pm PT]

[Karan Goel]

Hi everyone,

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

We'll be joined by Cody Coleman, who will talk about data selection methods for machine learning. 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=8bNwn8EHqOU

Guests: Cody Coleman

Title: Data selection for Data-Centric AI: Data Quality Over Quantity

Abstract: Data selection methods, such as active learning and core-set selection, improve the data efficiency of machine learning by identifying the most informative data points to label or train on. Across the data selection literature, there are many ways to identify these training examples. However, classical data selection methods are prohibitively expensive to apply in deep learning because of the larger datasets and models. This talk will describe two techniques to make data selection methods more tractable. First, "selection via proxy" (SVP) avoids expensive training and reduces the computation per example by using smaller proxy models to quantify the informativeness of each example. Second, "similarity search for efficient active learning and search" (SEALS) reduces the number of examples processed by restricting the candidate pool for labeling to the nearest neighbors of the currently labeled set instead of scanning over all of the unlabeled data. Both methods lead to order of magnitude performance improvements, making active learning applications on billions of unlabeled images practical for the first time.
Bio: Cody Coleman is the Founder and CEO of Coactive AI. He is also a co-creator of DAWNBench and MLPerf and a founding member of MLCommons. His work spans from performance benchmarking of machine learning systems to computationally efficient methods for active learning and core-set selection. He holds a PhD in Computer Science from Stanford University, where Professors Matei Zaharia and Peter Bailis advised him, and an MEng and BS from MIT.

See you all there!

Best,

Karan

[Karan Goel]

Reminder: we're starting in 15 minutes!