论文标题
高斯过程的提升
Gaussian Process Boosting
论文作者
论文摘要
我们介绍了一种新颖的方式，将增强功能与高斯工艺和混合效应模型相结合。首先，在高斯过程中，先前平均函数的零或线性假设可以放松，并以灵活的非参数方式分组随机效应模型，其次，在大多数增强算法中做出的独立性假设。前者有利于预测准确性和避免模型误差。后者对于有效学习固定效应预测函数和获得概率预测很重要。我们提出的算法也是一种用于处理培养中高心电性分类变量的新颖解决方案。此外，我们提出了一个扩展名，该扩展是使用维奇亚近似为高斯工艺模型缩放到大数据的，该模型依靠新的结果进行协方差参数推断。与多个模拟和现实世界数据集的现有方法相比，我们获得了提高的预测准确性。
We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean function in Gaussian process and grouped random effects models in a flexible non-parametric way and, second, the independence assumption made in most boosting algorithms. The former is advantageous for prediction accuracy and for avoiding model misspecifications. The latter is important for efficient learning of the fixed effects predictor function and for obtaining probabilistic predictions. Our proposed algorithm is also a novel solution for handling high-cardinality categorical variables in tree-boosting. In addition, we present an extension that scales to large data using a Vecchia approximation for the Gaussian process model relying on novel results for covariance parameter inference. We obtain increased prediction accuracy compared to existing approaches on multiple simulated and real-world data sets.
