Recyclable Gaussian Processes

• URL: http://arxiv.org/abs/2010.02554v1
• Date: Tue, 6 Oct 2020 09:01:55 GMT
• Title: Recyclable Gaussian Processes
• Authors: Pablo Moreno-Mu\~noz, Antonio Art\'es-Rodr\'iguez and Mauricio A. \'Alvarez
• Abstract summary: 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. Our framework allows for regression, classification and heterogeneous tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We exploit infinite-dimensional integral operators based on the Kullback-Leibler divergence between stochastic processes to re-combine arbitrary amounts of variational sparse approximations with different complexity, likelihood model and location of the pseudo-inputs. Extensive results illustrate the usability of our framework in large-scale distributed experiments, also compared with the exact inference models in the literature.

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