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Star Convexity and Robust Deep Learning: Two Perspectives from
Stochastic Optimization
I will discuss two recent research projects aiming at a better
understanding of stochastic optimization algorithms. The first project
tackles the challenge of "star-convex optimization", developing a new
randomized algorithm that for the first time allows efficient
optimization of a natural class of non-convex functions arising in
machine learning contexts. The second project is ongoing work
attempting to explain the mystery "why does deep learning generalize
so well?" We reveal a previously unknown side effect of stochastic
gradient descent that, under very broad conditions, nudges deep
learning models towards simpler hypotheses that generalize better.
Host: Professor Ugur Cetintemel
Computer Science at Brown University
Providence, Rhode Island 02912 USA
Phone: 401-863-7600
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