Thompson Sampling for Bandits with Clustered Arms
Paper i proceeding, 2021

We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered. We show, both theoretically and empirically, how exploiting a given cluster structure can significantly improve the regret and computational cost compared to using standard Thompson sampling. In the case of the stochastic multi-armed bandit we give upper bounds on the expected cumulative regret showing how it depends on the quality of the clustering. Finally, we perform an empirical evaluation showing that our algorithms perform well compared to previously proposed algorithms for bandits with clustered arms.


Emil Carlsson

Data Science och AI 1

Devdatt Dubhashi

Data Science och AI 1

Fredrik Johansson

Data Science och AI 1

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

978-0-9992411-9-6 (ISBN)

International Joint Conferences on Artificial Intelligence
Toronto, Canada,


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