{"id":574956,"date":"2019-02-05T09:00:43","date_gmt":"2019-02-05T17:00:43","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=574956"},"modified":"2019-09-01T07:37:17","modified_gmt":"2019-09-01T14:37:17","slug":"contextual-bandits-with-continuous-actions-smoothing-zooming-and-adapting","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/contextual-bandits-with-continuous-actions-smoothing-zooming-and-adapting\/","title":{"rendered":"Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting"},"content":{"rendered":"

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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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 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