Work

Kernel Multi-task Learning using Task-specific Features

Year: 2007

Type: article

Abstract: 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. 1

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

Institution: University of Edinburgh

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

OpenAlex ID: W1519342765

FWCI: 9.123

Cites: 17

Cited by: 74

Related to: 10