Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization

• URL: http://arxiv.org/abs/2205.08295v1
• Date: Tue, 17 May 2022 12:51:54 GMT
• Title: Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization
• Authors: Young-Geun Choi, Gi-Soo Kim, Seunghoon Paik and Myunghee Cho Paik
• Abstract summary: "SemiGraphTS" is a novel contextual Thompson-sampling algorithm for a graph-based semi-parametric reward model. We derive an upper bound of the cumulative regret that can be expressed as a multiple of a factor depending on the graph structure.
• Score: 9.864260997723976
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging. Several works have addressed the problem by investigating semi-parametric contextual bandits and warned that ignoring non-stationarity could harm performances. Another prevalent human behavior is social interaction which has become available in a form of a social network or graph structure. As a result, graph-based contextual bandits have received much attention. In this paper, we propose "SemiGraphTS," a novel contextual Thompson-sampling algorithm for a graph-based semi-parametric reward model. Our algorithm is the first to be proposed in this setting. We derive an upper bound of the cumulative regret that can be expressed as a multiple of a factor depending on the graph structure and the order for the semi-parametric model without a graph. We evaluate the proposed and existing algorithms via simulation and real data example.

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