Bandits for BMO Functions
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Abstract (arXiv)
We study the bandit problem where the underlying expected reward is a Bounded Mean Oscillation (BMO) function. BMO functions are allowed to be discontinuous and unbounded, and are useful in modeling signals with infinities in the do-main. We develop a toolset for BMO bandits, and provide an algorithm that can achieve poly-log δ-regret -- a regret measured against an arm that is optimal after removing a δ-sized portion of the arm space.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2.
3 papers.
- 2021Lipschitz Bandits with Batched Feedbackcited by 21
- 2023A Lipschitz Bandits Approach for Continuous Hyperparameter Optimizationcited by 9
- 2023From Random Search to Bandit Learning in Metric Measure Spacescited by 0
- 2023From Random Search to Bandit Learning in Metric Measure Spaces19 May 2023
- 2023A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization3 Feb 2023
- 2021Lipschitz Bandits with Batched Feedback19 Oct 2021