Learning the kernel parameters for Gaussian processes is often the
computational bottleneck in applications such as online learning, Bayesian
optimization, or active learning. Amortizing parameter inference over different
datasets is a promising approach to dramatically speed up training time.
However, existing methods restrict the amortized inference procedure to a fixed
kernel structure. The amortization network must be redesigned manually and
trained again in case a different kernel is employed, which leads to a large
overhead in design time and training time. We propose amortizing kernel
parameter inference over a complete kernel-structure-family rather than a fixed
kernel structure. We do that via defining an amortization network over pairs of
datasets and kernel structures. This enables fast kernel inference for each
element in the kernel family without retraining the amortization network. As a
by-product, our amortization network is able to do fast ensembling over kernel
structures. In our experiments, we show drastically reduced inference time
combined with competitive test performance for a large set of kernels and
datasets.

该论文提出了通过对数据集和核结构对进行分别设计和训练的自动编码特征提取网络来实现核参数推断之间的摊销，从而使得基于高斯过程的学习得以快速实现。

结构化内核高斯过程超参数的分摊推断

Amortized Inference for Gaussian Process Hyperparameters of Structured  Kernels

Despite its importance, choosing the structural form of the kernel in
nonparametric regression remains a black art. We define a space of kernel
structures which are built compositionally by adding and multiplying a small
number of base kernels. We present a method for searching over this space of
structures which mirrors the scientific discovery process. The learned
structures can often decompose functions into interpretable components and
enable long-range extrapolation on time-series datasets. Our structure search
method outperforms many widely used kernels and kernel combination methods on a
variety of prediction tasks.

本文提出一种通过组合基本核函数来寻找最优核函数的方法，使得拟合函数能够分解为易于理解的部分，从而实现对时间序列数据的长程外推，并在多种预测任务上表现出色。