An Interpretable and Sample Efficient Deep Kernel for Gaussian Process

Yijue Dai, Tianjian Zhang, Zhidi Lin, Feng Yin, Sergios Theodoridis, Shuguang Cui

Abstract: We propose a novel Gaussian process kernel that takes advantage of a deep neural network (DNN) structure but retains good interpretability. The resulting kernel is capable of addressing four major ...