论文标题
美元
$\{\text{PF}\}^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
论文作者
论文摘要
我们提出可行的帕累托边境熵搜索（$ \ {\ text {pf} \}^2 $ ES） - 多目标贝叶斯优化支持未知约束和批次查询的新型信息理论获取功能。由于表征候选评估和（可行的）帕累托前沿之间的相互信息的复杂性，现有方法必须采用严重阻碍其性能的粗略近似值，要么依赖于昂贵的推理方案，从而大大增加了优化的计算机间开销。相反，通过使用变分的下限，$ \ {\ text {pf} \}^2 $ es提供了相互信息的低成本和准确估计。我们根据其他信息理论获取功能进行基准基准$ \ {\ text {pf} \}^2 $ es，这证明了其在合成和现实世界设计问题之间优化的竞争性能。
We present Parallel Feasible Pareto Frontier Entropy Search ($\{\text{PF}\}^2$ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch query. Due to the complexity of characterizing the mutual information between candidate evaluations and (feasible) Pareto frontiers, existing approaches must either employ crude approximations that significantly hamper their performance or rely on expensive inference schemes that substantially increase the optimization's computational overhead. By instead using a variational lower bound, $\{\text{PF}\}^2$ES provides a low-cost and accurate estimate of the mutual information. We benchmark $\{\text{PF}\}^2$ES against other information-theoretic acquisition functions, demonstrating its competitive performance for optimization across synthetic and real-world design problems.
