On parallelizing multi-task bayesian optimization

Parallelizing Bayesian Optimization has recently attracted a lot of attention. The challenge is usually to estimate the effect multiple new samples will have on the posterior distribution of the objective function, and the combinatorial explosion of the possible sample locations. In this paper, we show that at least for multi-task Bayesian Optimization, parallelization is straightforward because the benefit of samples is independent as long as...