Research Paper#Bandit Algorithms, Machine Learning, Dimensionality Reduction🔬 ResearchAnalyzed: Jan 3, 2026 08:49
Optimal Single-Index Bandit Algorithm Overcoming Dimensionality Curse
Published:Dec 31, 2025 06:48
•1 min read
•ArXiv
Analysis
This paper presents a novel single-index bandit algorithm that addresses the curse of dimensionality in contextual bandits. It provides a non-asymptotic theory, proves minimax optimality, and explores adaptivity to unknown smoothness levels. The work is significant because it offers a practical solution for high-dimensional bandit problems, which are common in real-world applications like recommendation systems. The algorithm's ability to adapt to unknown smoothness is also a valuable contribution.
Key Takeaways
Reference
“The algorithm achieves minimax-optimal regret independent of the ambient dimension $d$, thereby overcoming the curse of dimensionality.”