Kernel multi-task learning using task-specific features

Edwin V. Bonilla, Felix V. Agakov, Christopher K.I. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this paper we are concerned with multitask 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.
Original languageEnglish
Title of host publicationProceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
EditorsMarina Meila, Xiaotong Shen
PublisherJournal of Machine Learning Research: Workshop and Conference Proceedings
Number of pages8
Publication statusPublished - 2007


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