论文标题
通过多样本超级核武器改善帕累托前沿学习
Improving Pareto Front Learning via Multi-Sample Hypernetworks
论文作者
论文摘要
最近将Pareto Front Learning（PFL）作为一种有效的方法引入，以获取从给定的权衡矢量到Pareto Front上的解决方案，该方法解决了多目标优化（MOO）问题。由于目标相互冲突之间的固有权衡，PFL在许多情况下提供了一种灵活的方法，在许多情况下，决策者无法指定一个帕累托解决方案而不是另一个帕累托解决方案，并且必须根据情况而切换它们。但是，现有的PFL方法忽略了在优化过程中解决方案之间的关系，这阻碍了获得的前面质量。为了克服这个问题，我们提出了一个新颖的PFL框架，即PHN-HVI，该框架采用了超级net工作来从一系列不同的权衡偏好中生成多种解决方案，并通过最大化这些解决方案定义的高量指标来提高帕累托阵线的质量。几个MOO机器学习任务的实验结果表明，所提出的框架在生产权衡帕累托方面的基础大大优于基准。
Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.
