A Universal Law of Robustness via Isoperimetry with Sebastien Bubeck – #551

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 work being done in the adversarial robustness community.

The complete show notes for this episode can be found at https://twimlai.com/go/551

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