The University of Southampton
University of Southampton Institutional Repository

Towards pareto descent directions in sampling experts for multiple tasks in an on-line learning paradigm

Towards pareto descent directions in sampling experts for multiple tasks in an on-line learning paradigm
Towards pareto descent directions in sampling experts for multiple tasks in an on-line learning paradigm
In many real-life design problems, there is a requirement to simultaneously balance multiple tasks or objectives in the system that are conflicting in nature, where minimizing one objective causes another to increase in value, thereby resulting in trade-offs between the objectives. For example, in embedded multi-core mobile devices and very large scale data centers, there is a continuous problem of simultaneously balancing interfering goals of maximal power savings and minimal performance delay with varying trade-off values for different application workloads executing on them. Typically, the optimal trade-offs for the executing workloads, lie on a difficult to determine optimal Pareto front. The nature of the problem requires learning over the lifetime of the mobile device or server with continuous evaluation and prediction of the trade-off settings on the system that balances the interfering objectives optimally. Towards this, we propose an on-line learning method, where the weights of experts for addressing the objectives are updated based on a convex combination of their relative performance in addressing all objectives simultaneously. An additional importance vector that assigns relative importance to each objective at every round is used, and is sampled from a convex cone pointed at the origin Our preliminary results show that the convex combination of the importance vector and the gradient of the potential functions of the learner's regret with respect to each objective ensure that in the next round, the drift (instantaneous regret vector), is the Pareto descent direction that enables better convergence to the optimal Pareto front.
SS-13-05
AAAI Press
Ghosh, Shaona
b6567624-3b1f-40c2-9de7-fd44536a94a9
Lovell, Christopher James
e894d207-d2e7-4bb3-b39d-ea62f204140c
Gunn, Steve R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Ghosh, Shaona
b6567624-3b1f-40c2-9de7-fd44536a94a9
Lovell, Christopher James
e894d207-d2e7-4bb3-b39d-ea62f204140c
Gunn, Steve R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868

Ghosh, Shaona, Lovell, Christopher James and Gunn, Steve R. (2013) Towards pareto descent directions in sampling experts for multiple tasks in an on-line learning paradigm. In Proceedings of the AAAI Spring Symposium Series of Lifelong Machine Learning 2013. vol. 13, AAAI Press..

Record type: Conference or Workshop Item (Paper)

Abstract

In many real-life design problems, there is a requirement to simultaneously balance multiple tasks or objectives in the system that are conflicting in nature, where minimizing one objective causes another to increase in value, thereby resulting in trade-offs between the objectives. For example, in embedded multi-core mobile devices and very large scale data centers, there is a continuous problem of simultaneously balancing interfering goals of maximal power savings and minimal performance delay with varying trade-off values for different application workloads executing on them. Typically, the optimal trade-offs for the executing workloads, lie on a difficult to determine optimal Pareto front. The nature of the problem requires learning over the lifetime of the mobile device or server with continuous evaluation and prediction of the trade-off settings on the system that balances the interfering objectives optimally. Towards this, we propose an on-line learning method, where the weights of experts for addressing the objectives are updated based on a convex combination of their relative performance in addressing all objectives simultaneously. An additional importance vector that assigns relative importance to each objective at every round is used, and is sampled from a convex cone pointed at the origin Our preliminary results show that the convex combination of the importance vector and the gradient of the potential functions of the learner's regret with respect to each objective ensure that in the next round, the drift (instantaneous regret vector), is the Pareto descent direction that enables better convergence to the optimal Pareto front.

Text
LearningExpertsForMultipleCriteria.pdf - Version of Record
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 18 January 2013
Published date: 25 March 2013
Venue - Dates: AAAI Spring Symposium on Lifelong Machine Learning 2013, United States, 2013-03-25 - 2013-03-27
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 346553
URI: https://eprints.soton.ac.uk/id/eprint/346553
PURE UUID: 96f0e63a-0304-4499-97fa-3817101dee74

Catalogue record

Date deposited: 08 Apr 2013 09:28
Last modified: 19 Jul 2019 21:47

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×