Prakhar Singhvi

Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits

We study Bayesian linear bandits with an isotropic Gaussian parameter, independent Gaussian candidate arms, and Gaussian reward noise when the horizon is proportional to the dimension. The normalized posterior uncertainty has an explicit limit that is uniform over all causal policies. Gaussian posterior identities then...