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The Ethereal
An optimal algorithm for bandit convex optimization
March 14, 2016 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Elad Hazan, Yuanzhi Li
arXiv ID
1603.04350
Category
cs.LG: Machine Learning
Cross-listed
cs.DS
Citations
54
Venue
arXiv.org
Last Checked
5 months ago
Abstract
We consider the problem of online convex optimization against an arbitrary adversary with bandit feedback, known as bandit convex optimization. We give the first $\tilde{O}(\sqrt{T})$-regret algorithm for this setting based on a novel application of the ellipsoid method to online learning. This bound is known to be tight up to logarithmic factors. Our analysis introduces new tools in discrete convex geometry.
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