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.