  We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch
sampling algorithm for Bayesian Optimization (BO). KMBBO uses unsupervised
learning to efficiently estimate peaks of the model acquisition function. We
show in empirical experiments that our method outperforms the current
state-of-the-art batch allocation algorithms on a variety of test problems
including tuning of algorithm hyper-parameters and a challenging drug discovery
problem. In order to accommodate the real-world problem of high dimensional
data, we propose a modification to KMBBO by combining it with compressed
sensing to project the optimization into a lower dimensional subspace. We
demonstrate empirically that this 2-step method outperforms algorithms where no
dimensionality reduction has taken place.
