  Thompson sampling is an efficient algorithm for sequential decision making,
which exploits the posterior uncertainty to address the
exploration-exploitation dilemma. There has been significant recent interest in
integrating Bayesian neural networks into Thompson sampling. Most of these
methods rely on global variable uncertainty for exploration. In this paper, we
propose a new probabilistic modeling framework for Thompson sampling, where
local latent variable uncertainty is used to sample the mean reward.
Variational inference is used to approximate the posterior of the local
variable, and semi-implicit structure is further introduced to enhance its
expressiveness. Our experimental results on eight contextual bandit benchmark
datasets show that Thompson sampling guided by local uncertainty achieves
state-of-the-art performance while having low computational complexity.
