Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, computer vision and wireless networks to list a few. To efficiently train machine learning models under such high labeling costs, active learning (AL) judiciously selects the most informative data instances to label on-the-fly. This active sampling process can benefit from a statistical function model, that is typically captured by a Gaussian process (GP) with well-documented merits especially in the regression task. While most GP-based AL approaches rely on a single kernel function, the present contribution advocates an ensemble of GP (EGP) models with weights adapted to the labeled data collected incrementally. Building on this novel EGP model, a suite of acquisition functions emerges based on the uncertainty and disagreement rules. An adaptively weighted ensemble of EGP-based acquisition functions is advocated to further robustify performance. Extensive tests on synthetic and real datasets in the regression task showcase the merits of the proposed EGP-based approaches with respect to the single GP-based AL alternatives.

中文翻译：

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用于自适应主动学习的加权集成


在多个应用领域，包括医学成像、机器人、计算机视觉和无线网络等，获取标记数据的成本可能很高。为了在如此高的标记成本下有效地训练机器学习模型，主动学习（AL）会明智地选择信息最丰富的数据实例来进行即时标记。这种主动采样过程可以受益于统计函数模型，该模型通常由高斯过程（GP）捕获，具有详细记录的优点，尤其是在回归任务中。虽然大多数基于 GP 的 AL 方法依赖于单个核函数，但目前的贡献提倡采用 GP (EGP) 模型的集合，其权重适应增量收集的标记数据。在这种新颖的 EGP 模型的基础上，基于不确定性和分歧规则出现了一套采集函数。提倡使用基于 EGP 的采集函数的自适应加权集合来进一步增强性能。回归任务中对合成数据集和真实数据集的广泛测试展示了所提出的基于 EGP 的方法相对于单一基于 GP 的 AL 替代方案的优点。