Can I read Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization on EtoBox?

Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization by Choi, Young-Geun; Kim, Gi-Soo; Paik, Seunghoon; Paik, Myunghee Cho is a scholarly article available to read on EtoBox.

What is Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization about?

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.

Author

Choi, Young-Geun; Kim, Gi-Soo; Paik, Seunghoon; Paik, Myunghee Cho

Published

2022

Language

EN