Inthis paper, to overcome these limitations, we propose a new algorithm based on the principle ofoptimism inthefaceofuncertainty.

Inthis paper, to overcome these limitations, we propose a new algorithm based on the principle ofoptimism inthefaceofuncertainty.

In the stochastic linear contextual bandit setting there exist several minimax procedures for exploration with policies that are reactive to the data being acquired.

We develop a class of informationtheoretic Bayesian regret bounds that nearly match existing lower bounds on a variety ofproblem instances, demonstrating theadaptivity ofIDS. Toefficiently implement sparse IDS, we propose an empirical Bayesian approach for sparse posterior sampling using a spike-and-slab Gaussian-Laplace prior. Numerical results demonstrate significant regretreductions bysparseIDSrelativetoseveral baselines.