Topic: Multi-output Gaussian process models

The activity of 100s-1000’s of neurophysiological signals can be recorded during behaviors and in response to sensory stimuli, creating high-dimensional time-series data. Understanding such data is often aided by methods that extract low-dimensional latent structure present in the high-dimensional recordings. Multi-output Gaussian process models leverage the nonparametric nature of Gaussian processes to capture structure across multiple outputs. However, this class of models typically assumes that the correlations between the output response variables are invariant in the input space (e.g., se