Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this paper, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, configuration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity.

翻译：高斯过程（GP）代理模型的主动学习在优化物理/计算机模拟实验的实验设计以及引导机器学习中的数据采集方案方面具有重要价值。本文提出了一种用于主动学习分段跳跃高斯过程（Jump GP）代理模型的方法。跳跃高斯过程在单个设计空间区域内连续，但在不同区域间具有间断性，这种特性使其适用于自主材料设计、智能工厂系统配置等诸多应用场景。尽管本文采用的主动学习启发式方法借鉴了原本为普通高斯过程设计的策略，但我们证明在跳跃高斯过程背景下，除了常规的模型不确定性外，额外考虑模型偏差至关重要。为此，我们开发了跳跃高斯过程模型的偏差与方差估计器。通过一系列合成基准测试和不同复杂度的真实模拟实验，展示了所提方法的优势并提供了实证依据。