Multi-objective Bayesian optimization aims to find the Pareto front of optimal trade-offs between a set of expensive objectives while collecting as few samples as possible. In some cases, it is possible to evaluate the objectives separately, and a different latency or evaluation cost can be associated with each objective. This presents an opportunity to learn the Pareto front faster by evaluating the cheaper objectives more frequently. We propose a scalarization based knowledge gradient acquisition function which accounts for the different evaluation costs of the objectives. We prove consistency of the algorithm and show empirically that it significantly outperforms a benchmark algorithm which always evaluates both objectives.

翻译：多目标贝叶斯优化旨在在尽可能少采样的情况下，找到一组昂贵目标之间最优折衷的帕累托前沿。在某些情况下，可以对目标进行单独评估，且每个目标可能具有不同的延迟或评估成本。这为通过更频繁地评估成本较低的目标来更快地学习帕累托前沿提供了可能性。我们提出了一种基于标量化的知识梯度采集函数，该函数考虑了目标的不同评估成本。我们证明了该算法的一致性，并通过实验表明，它在性能上显著优于始终评估两个目标的基准算法。