Recyclable Gaussian Processes

Understand

We present a new framework for recycling independent variational approximations to Gaussian processes.

• The main contribution is the construction of variational ensembles given a dictionary of fitted Gaussian processes without revisiting any subset of observations.
• Our framework allows for regression, classification and heterogeneous tasks, i.e.
• mix of continuous and discrete variables over the same input domain.