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Regret Analysis of the Anytime Optimally Confident UCB Algorithm

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arxiv 1603.08661 v2 pith:RN2T653A submitted 2016-03-29 cs.LG math.STstat.MLstat.TH

Tor Lattimore This is my paper · ORCID
classification cs.LGmath.STstat.MLstat.TH
keywords algorithmregretanytimeboundconfidentfinite-timeoptimallyanalyse
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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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    cs.IR 2019-08 conditional novelty 4.0 of 10

    A Vrbo team used daily Thompson sampling to rank four recommendation models by click-through rate, but the A/B validation they report is for a previous campaign's winner, not the current one.

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