PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2989
Deposited By:Christopher Williams
Deposited On:02 May 2007