arXiv:2606.08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this paper. In this setting, we are given $n$ arms with unknown sub-Gaussian reward distributions and a parameter $W$. The arms arrive in a single-pass stream, and only the most recent $W$ arms are considered valid. The algorithm is required to perform pure exploration and regret minimization with limited memory, defined as the number of stored arms. The model is a natural extension of the streaming mult
The continuous growth of online data streams and the real-world application of AI in dynamic environments necessitate more sophisticated online learning algorithms capable of handling recency effects.
This research provides foundational algorithmic improvements for AI systems operating on streaming data, potentially enhancing their adaptability and efficiency in real-time decision-making scenarios.
The development of effective sliding-window streaming multi-armed bandit algorithms will improve the ability of AI agents to learn and adapt in environments where older data quickly loses relevance.
- · AI algorithm developers
- · Companies with streaming data analytics needs
- · Personalized recommendation systems
- · Online advertising platforms
- · AI systems relying on static or batch learning
- · Inefficient online learning algorithms
Improved performance of AI agents in dynamic, real-time environments where data recency is critical.
Faster adaptation and more efficient resource allocation for AI-powered autonomous systems and decision support.
Enhanced trust and broader adoption of AI agents in mission-critical applications due to their improved adaptability and accuracy.
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