Title: Differentiable Compositional Kernel Learning for Gaussian Processes,
Abstract: The generalization properties of Gaussian processes depend heavily on the
choice of kernel, and this choice remains a dark art. We present the Neural
Kernel Network (NKN), a flexible family of kernels represented by a neural
network. The NKN architecture is based on the composition rules for kernels, so
that each unit of the network corresponds to a valid kernel. It can compactly
approximate compositional kernel structures such as those used by the Automatic
Statistician (Lloyd et al., 2014), but because the architecture is
differentiable, it is end-to-end trainable with gradient-based optimization. We
show that the NKN is universal for the class of stationary kernels. Empirically
we demonstrate pattern discovery and extrapolation abilities of NKN on several
tasks that depend crucially on identifying the underlying structure, including
time series and texture extrapolation, as well as Bayesian optimization.