Multi-task Gaussian Process Prediction

In this paper we investigate multi-task learning in the context of Gaussian Pro-cesses (GP). We propose a model that learns a shared covariance function on input-dependent features and a “free-form ” covariance matrix over tasks. This al-lows for good flexibility when modelling inter-task dependencies while avoiding the need for large amounts of data for training. We show that under the assump-tion 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 trans-fer occurs. We evaluate the benefits of our model on two practical applications: a compiler performance prediction problem and an exam score prediction task.…

• EA
  Engineering and Physical Sciences Research Council