Emergent Mind
An optimal algorithm for bandit convex optimization
(1603.04350)
Published Mar 14, 2016
in
cs.LG
and
cs.DS
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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