Both Bayesian optimization and active learning realize an adaptive sampling scheme to achieve a specific learning goal. However, while the two fields have seen an exponential growth in popularity in the past decade, their dualism has received relatively little attention. In this position paper, we argue for an original unified perspective of Bayesian optimization and active learning based on the synergy between the principles driving the sampling policies. This symbiotic relationship is demonstrated through the substantial analogy between the infill criteria of Bayesian optimization and the learning criteria in active learning, and is formalized for the case of single information source and when multiple sources at different levels of fidelity are available. We further investigate the capabilities of each infill criteria both individually and in combination on a variety of analytical benchmark problems, to highlight benefits and limitations over mathematical properties that characterize real-world applications.
翻译:贝叶斯优化和积极学习都实现一个适应性抽样计划,以实现具体的学习目标,然而,虽然在过去十年中,这两个领域都出现了受欢迎程度的指数增长,但它们的二元主义却相对没有受到多少关注。在本立场文件中,我们主张根据推动抽样政策的原则之间的协同作用,对巴伊斯优化和积极学习形成一个最初的统一观点。这种共生关系表现在巴伊斯优化的填充标准与积极学习的学习标准之间的实质性类比中,对于单一信息来源和不同水平的多种来源而言,这种共生关系是正式化的。我们进一步调查每个填充标准的能力,无论是单独还是结合各种分析基准问题,以突出作为现实世界应用特征的数学特性的好处和局限性。</s>