We present a scalable Gaussian process model for identifying and
characterizing smooth multidimensional changepoints, and automatically learning
changes in expressive covariance structure. We use Random Kitchen Sink features
to flexibly define a change surface in combination with expressive spectral
mixture kernels to capture the complex statistical structure. Finally, through
the use of novel methods for additive non-separable kernels, we can scale the
model to large datasets. We demonstrate the model on numerical and real world
data, including a large spatio-temporal disease dataset where we identify
previously unknown heterogeneous changes in space and time.

提出一种可扩展的高斯过程模型，用于确定和表征平滑多维变化点，并自动学习表达协方差结构中的变化。通过 Random Kitchen Sink 特征来灵活地定义变化表面，结合表现力强的谱混合核来捕获复杂的统计结构，最后通过新颖的加性非可分离核方法，可以将模型扩展到大型数据集。我们在数值和实际数据上演示了该模型，包括一个大型时空疾病数据集，在其中确定了先前未知的空间和时间异质性变化。