<ul><li>Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology.</li><li>A simple and efficient approach is proposed to extend Bayesian optimization to large-scale batch evaluation.</li><li>The approach involves drawing a batch of axis-aligned subspaces of the original problem and selecting one acquisition point from each subspace.</li><li>Numerical experiments show that the proposed approach significantly speeds up convergence and performs competitively compared to other batch Bayesian optimization algorithms.</li></ul>

A Simple and Efficient Approach to Batch Bayesian Optimization

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