  Scaling Bayesian optimization to high dimensions is challenging task as the
global optimization of high-dimensional acquisition function can be expensive
and often infeasible. Existing methods depend either on limited active
variables or the additive form of the objective function. We propose a new
method for high-dimensional Bayesian optimization, that uses a dropout strategy
to optimize only a subset of variables at each iteration. We derive theoretical
bounds for the regret and show how it can inform the derivation of our
algorithm. We demonstrate the efficacy of our algorithms for optimization on
two benchmark functions and two real-world applications- training cascade
classifiers and optimizing alloy composition.
