26 min listen
A Universal Law of Robustness via Isoperimetry with Sebastien Bubeck - #551
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
A Universal Law of Robustness via Isoperimetry with Sebastien Bubeck - #551
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
39 minutes
Released:
Jan 10, 2022
Format:
Podcast episode
Description
Today we’re joined by Sebastian Bubeck a sr principal research manager at Microsoft, and author of the paper A Universal Law of Robustness via Isoperimetry, a NeurIPS 2021 Outstanding Paper Award recipient. We begin our conversation with Sebastian with a bit of a primer on convex optimization, a topic that hasn’t come up much in previous interviews. We explore the problem that convex optimization is trying to solve, the application of convex optimization to multi-armed bandit problems, metrical task systems and solving the K-server problem. We then dig into Sebastian’s paper, which looks to prove that for a broad class of data distributions and model classes, overparameterization is necessary if one wants to interpolate the data. Finally, we discussed the relationship between the paper and the work being done in the adversarial robustness community.
The complete show notes for this episode can be found at twimlai.com/go/551
The complete show notes for this episode can be found at twimlai.com/go/551
Released:
Jan 10, 2022
Format:
Podcast episode
Titles in the series (100)
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