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• W2968743040 abstract "A stochastic multi-armed bandit problem with side information on the similarity and dissimilarity across different arms is considered. The action space of the problem can be represented by a unit interval graph (UIG) where each node represents an arm and the presence (absence) of an edge between two nodes indicates similarity (dissimilarity) between their mean rewards. Two settings of complete and partial side information based on whether the UIG is fully revealed are studied and a general two-step learning structure consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is proposed to fully exploit the topological structure of the side information. In both cases, the computation efficiency and the order optimality of the proposed learning policies in terms of both the size of the action space and the time length are established." @default.
• W2968743040 created "2019-08-22" @default.
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• W2968743040 date "2020-07-01" @default.
• W2968743040 modified "2023-10-03" @default.
• W2968743040 title "Multi-Armed Bandits on Partially Revealed Unit Interval Graphs" @default.
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• W2968743040 doi "https://doi.org/10.1109/tnse.2019.2935256" @default.
• W2968743040 hasPublicationYear "2020" @default.
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