Kernel Multi-task Learning using Task-specific Features
Edwin Bonilla, Felix Agakov and Christopher Williams
In: Eleventh International Conference on Artificial Intelligence and Statistics, 21-24 Mar 2007, San Juan, Puerto Rico.
In this paper we are concerned with multi-task learning
when task-specific features are available. We describe two
ways of achieving this using Gaussian process predictors:
in the first method, the data from all tasks is combined
into one dataset, making use of the task-specific features.
In the second method we train specific predictors for each
reference task, and then combine their predictions using
a gating network. We demonstrate these methods on a
compiler performance prediction problem, where a task is
defined as predicting
the speed-up obtained when applying a sequence of code
transformations to a given program.