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• W3203522710 abstract "We consider a contextual bandit problem with a combinatorial action set and time-varying base arm availability. At the beginning of each round, the agent observes the set of available base arms and their contexts and then selects an action that is a feasible subset of the set of available base arms to maximize its cumulative reward in the long run. We assume that the mean outcomes of base arms are samples from a Gaussian Process indexed by the context set ${cal X}$, and the expected reward is Lipschitz continuous in expected base arm outcomes. For this setup, we propose an algorithm called Optimistic Combinatorial Learning and Optimization with Kernel Upper Confidence Bounds (O'CLOK-UCB) and prove that it incurs $tilde{O}(Ksqrt{Toverline{gamma}_{T}} )$ regret with high probability, where $overline{gamma}_{T}$ is the maximum information gain associated with the set of base arm contexts that appeared in the first $T$ rounds and $K$ is the maximum cardinality of any feasible action over all rounds. To dramatically speed up the algorithm, we also propose a variant of O'CLOK-UCB that uses sparse GPs. Finally, we experimentally show that both algorithms exploit inter-base arm outcome correlation and vastly outperform the previous state-of-the-art UCB-based algorithms in realistic setups." @default.
• W3203522710 created "2021-10-11" @default.
• W3203522710 creator A5031100822 @default.
• W3203522710 creator A5053015746 @default.
• W3203522710 creator A5056816244 @default.
• W3203522710 date "2021-10-05" @default.
• W3203522710 modified "2023-09-27" @default.
• W3203522710 title "Contextual Combinatorial Volatile Bandits via Gaussian Processes" @default.
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• W3203522710 hasPublicationYear "2021" @default.
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