A Closer Look at Small-loss Bounds for Bandits with Graph Feedback

2 Feb 2020Chung-Wei LeeHaipeng LuoMengxiao Zhang

We study small-loss bounds for adversarial multi-armed bandits with graph feedback, that is, adaptive regret bounds that depend on the loss of the best arm or related quantities, instead of the total number of rounds. We derive the first small-loss bound for general strongly observable graphs, resolving an open problem of Lykouris et al. (2018)... (read more)

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