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Statistical efficiency of Thompson sampling for combinatorial semi-bandits

Abstract : We investigate stochastic combinatorial multi-armed bandit with semi-bandit feedback (CMAB). In CMAB, the question of the existence of an efficient policy with an optimal asymptotic regret (up to a factor poly-logarithmic with the action size) is still open for many families of distributions, including mutually independent outcomes, and more generally the multivariate sub-Gaussian family. We propose to answer the above question for these two families by analyzing variants of the Combinatorial Thompson Sampling policy (CTS). For mutually independent outcomes in $[0,1]$, we propose a tight analysis of CTS using Beta priors. We then look at the more general setting of multivariate sub-Gaussian outcomes and propose a tight analysis of CTS using Gaussian priors. This last result gives us an alternative to the Efficient Sampling for Combinatorial Bandit policy (ESCB), which, although optimal, is not computationally efficient.
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Contributor : Michal Valko Connect in order to contact the contributor
Submitted on : Friday, July 16, 2021 - 3:55:03 PM
Last modification on : Tuesday, January 18, 2022 - 2:26:08 PM
Long-term archiving on: : Sunday, October 17, 2021 - 7:04:54 PM


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  • HAL Id : hal-03288983, version 1
  • ARXIV : 2006.06613



Pierre H Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, et al.. Statistical efficiency of Thompson sampling for combinatorial semi-bandits. Neural Information Processing Systems Conference, Dec 2020, Virtual, France. ⟨hal-03288983⟩



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