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Regret Analysis of the Anytime Optimally Confident UCB Algorithm (1603.08661v2)
Published 29 Mar 2016 in cs.LG, math.ST, stat.ML, and stat.TH
Abstract: I introduce and analyse an anytime version of the Optimally Confident UCB (OCUCB) algorithm designed for minimising the cumulative regret in finite-armed stochastic bandits with subgaussian noise. The new algorithm is simple, intuitive (in hindsight) and comes with the strongest finite-time regret guarantees for a horizon-free algorithm so far. I also show a finite-time lower bound that nearly matches the upper bound.
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