3 accepted papers
In this paper, we introduce and analyze a variant of the Thompson sampling (TS) algorithm for contextual bandits. At each round, traditional TS requires samples from the current posterior distribution, which is usually intractable. To circumvent this issue, approximate inference techniques can be us…
Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a loss that is (typically) the reverse Kullback-Leibler (KL) divergence. Despite its empirical success, the theoretical p…
In this paper, we consider high dimensional contextual bandit problems. Within this setting, Thompson Sampling and its variants have been proposed and have been successfully applied to multiple machine learning problems. Existing theory on Thompson Sampling shows that it has suboptimal dimension dep…