GPTreeO让高斯过程回归能持续学习，自动优化参数并灵活控制计算效率与精度。
GPTreeO: An R package for continual regression with dividing local Gaussian processes
- 基于动态二叉树结构构建局部高斯过程，随数据流持续更新模型。
- 支持超参数持续优化与不确定性校准，提升长期预测稳定性。
- 模块化设计可接入任意高斯过程库，适合需要自定义的科研与工程用户。
我们提出 GPTreeO，一个面向可扩展高斯过程（GP）回归的灵活 R 语言包，专为持续学习场景设计。GPTreeO 基于分治局部高斯过程（DLGP）算法，通过持续输入数据流动态构建局部 GP 回归器的二叉树结构。在原始 DLGP 基础上，我们扩展了超参数持续优化功能，引入不确定性校准机制，并提出新的局部划分策略。此外，其模块化代码结构允许用户接入任意偏好使用的高斯过程库实现局部回归。GPTreeO 的灵活性使用户能够精细调控计算速度、精度、稳定性和平滑性之间的平衡。我们进行了敏感性分析，验证了其可配置特性在持续学习设定下的性能影响。
原文摘要 · Abstract (English)
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.
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