2019/05/23 by Thodoris Lykouris, Éva Tardos, Lykouris, Thodoris +3 · 2 citations
Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.1905.09898
openalex publication_date 2019/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the stochastic multi-armed bandit problem with the graph-based feedback structure introduced by Mannor and Shamir. We analyze the performance of the two most prominent stochastic bandit algorithms, Thompson Sampling and Upper Confidence Bound (UCB), in the graph-based feedback setting. We show that these algorithms achieve regret guarantees that combine the graph structure and the gaps between the means of the arm distributions. Surprisingly this holds despite the fact that these algorithms do not explicitly use the graph structure to select arms; they observe the additional feedback but do not explore based on it. Towards this result we introduce a "layering technique" highlighting the commonalities in the two algorithms.