arXiv:2605.20269v1 Announce Type: new Abstract: Many bandit deployments (recommendation, clinical dosing, ad targeting) share two facts prior work handles only in isolation: rewards live on a low-dimensional latent subspace, and that subspace drifts. Stationary low-rank bandits exploit rank but break under subspace change; non-stationary linear bandits adapt to drift but pay ambient rate $\widetilde{O}(d\sqrt{T})$. We study piecewise-stationary low-rank linear contextual bandits with scalar feedback: $\theta_t = B_k^\star w_t$ with rank-$r$ factor $B_k^\star\in\mathbb{R}^{d\times r}$ constant
This research addresses a critical limitation in existing bandit algorithms, which struggle with non-stationary environments, a common characteristic of real-world AI applications.
Improving the adaptability of AI algorithms to drifting data distributions is crucial for sustained performance in dynamic applications, impacting the effectiveness and reliability of AI deployments.
This advancement enables AI systems to better handle evolving user preferences or environmental shifts, leading to more robust and continuously learning systems without manual retraining.
- · AI researchers
- · Machine learning platforms
- · Companies deploying recommendation systems
- · Companies in ad-tech
- · Legacy stationary bandit algorithms
More efficient and adaptive low-rank bandit algorithms become available for practical applications.
Improved performance of AI systems in dynamic environments such as personalized recommendations or clinical dosing, requiring less human intervention.
Accelerated development of more sophisticated AI agents capable of continuous, autonomous adaptation in complex, unpredictable settings.
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Read at arXiv cs.LG