It is all in the noise: efficient multi-task Gaussian process inference with structured residuals

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Item Type:	Conference or Workshop Item
Title:	It is all in the noise: efficient multi-task Gaussian process inference with structured residuals
Creators Name:	Rakitsch, B., Lippert, C., Borgwardt, K. and Stegle, O.
Abstract:	Multi-task prediction methods are widely used to couple regressors or classification models by sharing information across related tasks. We propose a multi-task Gaussian process approach for modeling both the relatedness between regressors and the task correlations in the residuals, in order to more accurately identify true sharing between regressors. The resulting Gaussian model has a covariance term in form of a sum of Kronecker products, for which efficient parameter inference and out of sample prediction are feasible. On both synthetic examples and applications to phenotype prediction in genetics, we find substantial benefits of modeling structured noise compared to established alternatives.
Source:	Advances in Neural Information Processing Systems
Page Range:	1-9
Date:	December 2013
Official Publication:	https://papers.nips.cc/paper/5089-it-is-all-in-the-noise-efficient-multi-task-gaussian-process-inference-with-structured-residuals

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