论文标题
快速高斯过程通过局部交叉验证和预成立后的平均预测
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation
论文作者
论文摘要
高斯工艺（GPS）是贝叶斯非参数模型，可用于无数应用。尽管它们很受欢迎，但GP预测的成本（相对于培训点的数量，二次存储和立方复杂性）仍然是将GPS应用于大数据的障碍。我们提出了一种称为fastmuygps的快速后平均预测算法，以解决此缺点。 FastMuyGPS基于MUYGPS高参数估计算法，并利用了一对抛弃的交叉验证，批处理，最近的邻居的稀疏和预先计算来提供可扩展的快速GP预测。我们展示了几个基准，其中FastMuyGPS的预测具有卓越的准确性和竞争性或卓越的运行时与深神经网络和最先进的可扩展GP算法相比。
Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications. Despite their popularity, the cost of GP predictions (quadratic storage and cubic complexity with respect to the number of training points) remains a hurdle in applying GPs to large data. We present a fast posterior mean prediction algorithm called FastMuyGPs to address this shortcoming. FastMuyGPs is based upon the MuyGPs hyperparameter estimation algorithm and utilizes a combination of leave-one-out cross-validation, batching, nearest neighbors sparsification, and precomputation to provide scalable, fast GP prediction. We demonstrate several benchmarks wherein FastMuyGPs prediction attains superior accuracy and competitive or superior runtime to both deep neural networks and state-of-the-art scalable GP algorithms.
