Fighting Bandits with a New Kind of Smoothness

12/14/2015 ∙ by Jacob Abernethy, et al. ∙ 0

We define a novel family of algorithms for the adversarial multi-armed bandit problem, and provide a simple analysis technique based on convex smoothing. We prove two main results. First, we show that regularization via the Tsallis entropy, which includes EXP3 as a special case, achieves the Θ(√(TN)) minimax regret. Second, we show that a wide class of perturbation methods achieve a near-optimal regret as low as O(√(TN N)) if the perturbation distribution has a bounded hazard rate. For example, the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this key property.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.