Practical Contextual Bandits with Feedback Graphs

2023/02/17 by Zhang, Mengxiao, Zhang, Yuheng, Vrousgou, Olga +2 · 1 citation
Decision Sciences · Computer Science · Engineering · #Advanced Bandit Algorithms Research #Data Stream Mining Techniques #Smart Grid Energy Management

paper · pdf · doi:10.48550/arxiv.2302.08631

Abstract

While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which interpolates between the full information and bandit regimes, provides a promising framework to mitigate the statistical complexity of learning. In this paper, we propose and analyze an approach to contextual bandits with feedback graphs based upon reduction to regression. The resulting algorithms are computationally practical and achieve established minimax rates, thereby reducing the statistical complexity in real-world applications.

Cited by

• Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions

Related

• Efficient Contextual Bandits with Uninformed Feedback Graphs
• Stochastic contextual bandits with graph feedback: from independence number to MAS number
• Adversarial Linear Contextual Bandits with Graph-Structured Side Observations
• Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
• Stochastic Graph Bandit Learning with Side-Observations