PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-task Gaussian Process Prediction
Edwin Bonilla, Kian Ming Chai and Christopher Williams
In: Neural Information Processing Systems (NIPS) 2007, 3-6 Dec 2007, Vancouver, BC, Canada.


In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a shared covariance function on input-dependent features and a ``free-form'' covariance matrix over tasks. This allows for good flexibility when modelling inter-task dependencies while avoiding the need for large amounts of data for training. We show that under the assumption of noise-free observations and a block design, predictions for a given task only depend on its target values and therefore a cancellation of inter-task transfer occurs. We evaluate the benefits of our model on two practical applications: a compiler performance prediction problem and an exam score prediction task. Additionally, we make use of GP approximations and properties of our model in order to provide scalability to large data sets.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3442
Deposited By:Christopher Williams
Deposited On:11 February 2008