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- W2109690147 abstract "We study the adversarial multi-armed bandit problem in a setting where the player incurs a unit cost each time he switches actions. We prove that the player's T-round minimax regret in this setting is [EQUATION], thereby closing a fundamental gap in our understanding of learning with bandit feedback. In the corresponding full-information version of the problem, the minimax regret is known to grow at a much slower rate of Θ(√T). The difference between these two rates provides the first indication that learning with bandit feedback can be significantly harder than learning with full information feedback (previous results only showed a different dependence on the number of actions, but not on T.)" @default.
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- W2109690147 date "2014-05-31" @default.
- W2109690147 modified "2023-10-17" @default.
- W2109690147 title "Bandits with switching costs" @default.
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- W2109690147 doi "https://doi.org/10.1145/2591796.2591868" @default.
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