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.

本研究提出了GPTreeO，一个灵活的R包，旨在解决持续学习问题中的可扩展高斯过程回归。通过动态构建局部高斯回归器的二叉树，GPTreeO扩展了原有的DLGP算法，并优化了高斯过程超参数，最终在灵活性与性能之间提供了新的平衡解决方案。

GPTreeO：一个用于分割局部高斯过程的持续回归的R包