We introduce GPTreeO, a flexible R package for scalable Gaussian process (GP) regression, particularly tailored to continual learning problems. GPTreeO builds upon the Dividing Local Gaussian Processes (DLGP) algorithm, in which a binary tree of local GP regressors is dynamically constructed using a continual stream of input data. In GPTreeO we extend the original DLGP algorithm by allowing continual optimisation of the GP hyperparameters, incorporating uncertainty calibration, and introducing new strategies for how the local partitions are created. Moreover, the modular code structure allows users to interface their favourite GP library to perform the local GP regression in GPTreeO. The flexibility of GPTreeO gives the user fine-grained control of the balance between computational speed, accuracy, stability and smoothness. We conduct a sensitivity analysis to show how GPTreeO's configurable features impact the regression performance in a continual learning setting.

翻译：本文介绍GPTreeO，一个用于可扩展高斯过程回归的灵活R包，特别适用于连续学习问题。GPTreeO基于划分局部高斯过程算法构建，该算法通过连续输入数据流动态构建由局部GP回归器组成的二叉树。在GPTreeO中，我们扩展了原始DLGP算法，实现了GP超参数的连续优化，加入了不确定性校准，并提出了新的局部划分生成策略。此外，模块化的代码结构允许用户对接其偏好的GP库以执行GPTreeO中的局部GP回归。GPTreeO的灵活性使用户能够精细控制计算速度、精度、稳定性与平滑性之间的平衡。我们通过敏感性分析展示了GPTreeO的可配置特性如何影响连续学习场景下的回归性能。