Linear contextual bandits represent a fundamental class of models with numerous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance, ensuring provable guarantees for both worst-case constant-variance noise and deterministic reward scenarios.

Recommendation algorithms that select the most relevant item for sequentially arriving users or queries have become vital for navigating the internet and its many online platforms.

UCB, wherein agents cooperatively minimize the group regret under the coordination of a central server.

Inthis paper, to overcome these limitations, we propose a new algorithm based on the principle ofoptimism inthefaceofuncertainty.

To model the demand as a function of price and context, the existing literature either adopts a parametric model or a non-parametric model.