Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
arXiv:2606.27917v1 Announce Type: new Abstract: Contextual bandits with graph-structured arms arise in recommendation, citation retrieval, and social advertising, where arms connected on a graph tend to share reward signal. Standard dimensionality reduction ignores this structure, inflating exploration cost by a factor of $d/k$. We propose GraphDR-LinUCB, which projects arm features onto the graph's low-frequency spectral subspace and runs linear UCB in the resulting $k$-dimensional space. We prove the first $\wtO(k\sqrt{T})$ regret bound for spectral-projection-based contextual bandits, reduc
The paper presents refined algorithmic approaches for contextual bandits, leveraging graph structures to improve efficiency in AI applications.
Improved dimensionality reduction techniques for contextual bandits can significantly enhance the performance and resource efficiency of AI systems in real-world recommendation and retrieval tasks.
This research introduces concrete algorithmic advancements that could lead to more accurate and less computationally intensive AI systems for applications like personalized recommendations.
- · AI/ML researchers
- · Recommendation platforms
- · Social advertising companies
- · Less efficient bandit algorithms
- · High-compute AI infrastructure without optimization
More accurate and faster contextual bandit systems in production, leading to better user experiences.
Reduced operational costs for AI-driven platforms due to more efficient learning and decision-making.
Accelerated development of more adaptive and personalized AI applications across various industries, utilizing less data and compute.
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