Reviews: Bayesian Alignments of Warped Multi-Output Gaussian Processes

This submission presents a "three-layer" Gaussian process for multiple time-series analysis: a layer for transforming the input, a layer for convolutional GP, and a layer for warping the outputs. This is a different "twist" or "favour" of the existing deep-GP model. Approximate inference is via the scalable version of variational inference using inducing points. The authors state that one main contribution is the "closed-form solution for the \Phi -statistics for the convolution kernel". Experiments on a real data set from two wind turbines demonstrates its effectiveness over three existing models in terms of test-log-likelihoods. [Quality] This is a quality work, with clear model, approximation and experimental results.