  Best-arm identification (BAI) in a fixed-budget setting is a bandit problem
where the learning agent maximizes the probability of identifying the optimal
(best) arm after a fixed number of observations. Most works on this topic study
unstructured problems with a small number of arms, which limits their
applicability. We propose a general tractable algorithm that incorporates the
structure, by successively eliminating suboptimal arms based on their mean
reward estimates from a joint generalization model. We analyze our algorithm in
linear and generalized linear models (GLMs), and propose a practical
implementation based on a G-optimal design. In linear models, our algorithm has
competitive error guarantees to prior works and performs at least as well
empirically. In GLMs, this is the first practical algorithm with analysis for
fixed-budget BAI.
