Preliminary Empirical Study of Low-Rank, Hierarchical Gaussian Linear Bandits

Inspired by recent advances in multi-task bandits, we propose a new problem setting called low-rank, hierarchical Gaussian linear bandits, which combines low-rank structure with the hierarchical Bayesian approach. We extend the hierarchical Thompson sampling of Hong et al. [8] to our setting by combining it with Gibbs Sampling and show its efficacy empirically.