Novel Architecture of Deep Feature-Based Gaussian Processes with an Ensemble of Kernels

Yuanqing Song, Yuhao Liu, Petar M. Djuric

Abstract: The inherent adaptability and flexibility of Gaussian processes lie in the capability of their kernel functions to capture diverse data characteristics. Thus, selecting an appropriate kernel function is crucial because an improper choice can detrimentally affect the model’s performance. One way to enhance the capability of Gaussian processes in capturing intricate data features is by stacking multiple Gaussian processes, thus constructing deep Gaussian processes. In this paper, we propose a novel structure for deep Gaussian processes that uses an ensemble of diverse kernels. We demonstrate that this structure leads to superior results compared to those of a single-kernel deep Gaussian processes.