论文标题
联合熵搜索多目标贝叶斯优化
Joint Entropy Search for Multi-objective Bayesian Optimization
论文作者
论文摘要
许多现实世界中的问题都可以用作多目标优化问题，目标是确定竞争目标之间的最佳妥协集。多目标贝叶斯优化（BO）是一种样本有效的策略，可以部署以解决这些矢量价值优化问题，其中访问仅限于许多嘈杂的客观函数评估。在本文中，我们提出了一个新的信息理论获取功能，称为联合熵搜索（JES），该功能考虑了最佳输入和输出集的联合信息增益。我们向JES采集函数提供了几个分析近似，并向批处理设置引入了扩展。我们展示了这种新方法在Hypervolume及其加权变体方面对一系列合成和现实世界问题的有效性。
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.
