Contextual bandits with continuous actions: Smoothing, zooming, and adapting

Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
JournalJournal of Machine Learning Research
StatePublished - Jul 2020


  • Contextual bandits
  • Nonparametric learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability


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