Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards
<|reference_start|>Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards: We consider Kernelized Bandits (KBs) to optimize a function $f : \mathcal{X} \rightarrow [0,1]$ belonging to the Reproducing Kernel Hilbert Space (RKHS) $\mathcal{H}_k$. Mainstream works on kernelized bandits focus on a subgaussian noise model in which observations of the form $f(\mathbf{x}_t)+\epsilon_t$, being $\epsilon_t$ a subgaussian noise, are available (Chowdhury and Gopalan, 2017). Differently, we focus on the case in which we observe realizations $y_t \sim \text{Ber}(f(\mathbf{x}_t))$ sampled from a Bernoulli distribution with parameter $f(\mathbf{x}_t)$. While the Bernoulli model has been investigated successfully in multi-armed bandits (Garivier and Capp\'e, 2011), logistic bandits (Faury et al., 2022), bandits in metric spaces (Magureanu et al., 2014), it remains an open question whether tight results can be obtained for KBs. This paper aims to draw the attention of the online learning community to this open problem.<|reference_end|>
@article{mussi2024open,
    title={Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with
  Bernoulli Rewards},
    author={Marco Mussi and Simone Drago and Alberto Maria Metelli},
    journal={arXiv preprint arXiv:2407.06321},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06321},
    primaryClass={stat.ML cs.LG}
}