论文标题
优先探索有效的贝叶斯优化，并具有多种结果
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
论文作者
论文摘要
我们认为贝叶斯优化了昂贵的评估实验，这些实验产生了矢量值，决策者（DM）具有偏好。这些偏好是由效用函数编码的，该功能以封闭形式不知道，但可以通过要求DM表达对成对的结果向量的偏好来估算。为了解决这个问题，我们通过偏好探索开发贝叶斯优化，这是一个新颖的框架，通过成对的结果进行交互式实时偏好学习与DM之间的交替，以及与DM实用程序和结果的贝叶斯优化之间的成对比较。在此框架内，我们提出了专门为此任务设计的偏好探索策略，并通过大量的模拟研究来展示其性能。
We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences. These preferences are encoded by a utility function that is not known in closed form but can be estimated by asking the DM to express preferences over pairs of outcome vectors. To address this problem, we develop Bayesian optimization with preference exploration, a novel framework that alternates between interactive real-time preference learning with the DM via pairwise comparisons between outcomes, and Bayesian optimization with a learned compositional model of DM utility and outcomes. Within this framework, we propose preference exploration strategies specifically designed for this task, and demonstrate their performance via extensive simulation studies.
