Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives. Multi-objective Bayesian optimization (BO) is a sample efficient strategy that can be deployed to solve these vector-valued optimization problems where access is limited to a number of noisy objective function evaluations. In this paper, we propose a novel information-theoretic acquisition function for BO called Joint Entropy Search (JES), which considers the joint information gain for the optimal set of inputs and outputs. We present several analytical approximations to the JES acquisition function and also introduce an extension to the batch setting. We showcase the effectiveness of this new approach on a range of synthetic and real-world problems in terms of the hypervolume and its weighted variants.

翻译：许多现实世界问题可以被描述为一个多目标优化问题,其目标是确定相互竞争的目标之间的一套最佳折中办法。多目标巴耶斯优化(BO)是一种可被用于解决这些矢量估值优化问题的抽样高效战略,在这种问题上,准入仅限于若干噪音激烈的客观功能评估。在本文中,我们提议为BO设立一个称为联合通心搜索(JES)的新的信息理论获取功能,该功能将联合信息收益视为最佳的投入和产出组合。我们对JES获取功能提出若干分析近似值,并对批量设置进行扩展。我们用超量及其加权变量来展示这一新方法对于一系列合成和实际世界问题的有效性。