Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget. Previous work on active search has assumed access to a faithful (and expensive) oracle reporting experimental results. However, some settings offer access to cheaper surrogates such as computational simulation that may aid in the search. We propose a model of multifidelity active search, as well as a novel, computationally efficient policy for this setting that is motivated by state-of-the-art classical policies. Our policy is nonmyopic and budget aware, allowing for a dynamic tradeoff between exploration and exploitation. We evaluate the performance of our solution on real-world datasets and demonstrate significantly better performance than natural benchmarks.
翻译:积极搜索是一种学习模式,我们试图根据标签预算,确定尽可能多的稀有、有价值的类成员。 以往的积极搜索工作假定可以使用忠实(和昂贵)的甲骨文报告实验结果。 但是,有些环境提供了更廉价的替代机器人,例如计算模拟,这可能有助于搜索。 我们提出了多信仰积极搜索的模式,以及由最新传统政策驱动的这一环境的新颖的、有计算效率的政策。 我们的政策是非微型和预算意识的,允许在探索和开发之间进行动态的权衡。 我们评估了我们关于真实世界数据集的解决方案的绩效,并展示了比自然基准更好的业绩。