# Bandits for BMO Functions

arXiv: https://arxiv.org/abs/2007.08703 · HTML: https://mlsanity.com/paper/2007.08703/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Lipschitz Bandits with Batched Feedback](https://mlsanity.com/paper/2110.09722/) (2021)
- [A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization](https://mlsanity.com/paper/2302.01539/) (2023)
- [From Random Search to Bandit Learning in Metric Measure Spaces](https://mlsanity.com/paper/2305.11509/) (2023)

## 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.