Inter-domain Deep Gaussian Processes
This is an incremental improvement for researchers in Gaussian process modeling, addressing limitations in handling non-stationary data.
The paper tackled the problem of modeling non-stationary data with Gaussian processes by proposing Inter-domain Deep Gaussian Processes, which outperformed inter-domain shallow GPs and conventional DGPs on large-scale real-world datasets with global structure and high non-stationarity.
Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for modeling data exhibiting global structure but are limited to stationary covariance functions and thus fail to model non-stationary data effectively. We propose Inter-domain Deep Gaussian Processes, an extension of inter-domain shallow GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs), and demonstrate how to leverage existing approximate inference methods to perform simple and scalable approximate inference using inter-domain features in DGPs. We assess the performance of our method on a range of regression tasks and demonstrate that it outperforms inter-domain shallow GPs and conventional DGPs on challenging large-scale real-world datasets exhibiting both global structure as well as a high-degree of non-stationarity.