论文标题
可平行的多目标贝叶斯优化的可微分预期超量改进
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
论文作者
论文摘要
在许多实际情况下，决策者试图以样本有效的方式有效地优化多个竞争目标。多目标贝叶斯优化（BO）是一种常见的方法，但是许多表现最佳的采集功能没有知道的分析梯度，并且遭受了高计算开销的困扰。我们利用预期的Hypervolume改进（EHVI）（EHVI）来利用编程模型和硬件加速度的最新进展---以其较高的计算复杂性而臭名昭著的算法。我们得出了一种新型的Q指示超量改进（QEHVI）的公式，这是一种将EHVI扩展到平行，约束的评估设置的采集函数。 QEHVI是Q新候选点的关节EHVI（直至蒙特卡洛（MC）集成误差）的精确计算。尽管以前的EHVI公式依赖于无梯度的采集优化或近似梯度，但我们通过自动差异来计算MC估计器的精确梯度，从而使用一阶和Quasi-Secect-dord-dord-serd方法实现了有效和有效的优化。我们的经验评估表明，在许多实际情况下，Qehvi在计算方面都是可探讨的，并且在其壁时间的一小部分中优于最先进的多目标BO算法。
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization (BO) is a common approach, but many of the best-performing acquisition functions do not have known analytic gradients and suffer from high computational overhead. We leverage recent advances in programming models and hardware acceleration for multi-objective BO using Expected Hypervolume Improvement (EHVI)---an algorithm notorious for its high computational complexity. We derive a novel formulation of q-Expected Hypervolume Improvement (qEHVI), an acquisition function that extends EHVI to the parallel, constrained evaluation setting. qEHVI is an exact computation of the joint EHVI of q new candidate points (up to Monte-Carlo (MC) integration error). Whereas previous EHVI formulations rely on gradient-free acquisition optimization or approximated gradients, we compute exact gradients of the MC estimator via auto-differentiation, thereby enabling efficient and effective optimization using first-order and quasi-second-order methods. Our empirical evaluation demonstrates that qEHVI is computationally tractable in many practical scenarios and outperforms state-of-the-art multi-objective BO algorithms at a fraction of their wall time.
