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- W2912800932 abstract "We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively different regret bounds: one competes with a smoothed version of the policy class under no continuity assumptions, while the other requires standard Lipschitz assumptions. Both bounds exhibit data-dependent zooming behavior and, with no tuning, yield improved guarantees for benign problems. We also study adapting to unknown smoothness parameters, establishing a price-of-adaptivity and deriving optimal adaptive algorithms that require no additional information." @default.
- W2912800932 created "2019-02-21" @default.
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- W2912800932 date "2019-02-05" @default.
- W2912800932 modified "2023-09-27" @default.
- W2912800932 title "Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting" @default.
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