论文标题
贝叶斯优化高维空间的良好实践
Good practices for Bayesian Optimization of high dimensional structured spaces
论文作者
论文摘要
结构化但高维数据的可用性增加为优化开辟了新的机会。一个新兴和有希望的途径是探索无监督的方法，用于将结构化的高维数据投影到低维连续表示中，简化了优化问题并启用传统优化方法的应用。但是，这一研究纯粹是方法论上的，到目前为止，与从业者的需求几乎没有联系。在本文中，我们研究了在高维结构化数据集中执行贝叶斯优化的不同搜索空间设计选择的效果。特别是，我们分析了潜在空间的维度的影响，采集函数的作用并评估新方法以自动定义潜在空间中的优化界限。最后，基于使用合成和真实数据集的实验结果，我们为从业者提供建议。
The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervised methods for projecting structured high dimensional data into low dimensional continuous representations, simplifying the optimization problem and enabling the application of traditional optimization methods. However, this line of research has been purely methodological with little connection to the needs of practitioners so far. In this paper, we study the effect of different search space design choices for performing Bayesian Optimization in high dimensional structured datasets. In particular, we analyse the influence of the dimensionality of the latent space, the role of the acquisition function and evaluate new methods to automatically define the optimization bounds in the latent space. Finally, based on experimental results using synthetic and real datasets, we provide recommendations for the practitioners.
