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Bandit Convex Optimization: sqrt{T} Regret in One Dimension
Published on Aug 20, 20151774 Views
We analyze the minimax regret of the adversarial bandit convex optimization problem. Focusing on the one-dimensional case, we prove that the minimax regret is $\widetilde\Theta(\sqrt{T})$ and partiall
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Bandit Convex Optimization: √T Regret in One Dimension00:00
Bandit Convex Optimization00:08
Previous Results01:34
Main Ideas02:19