CUED Publications database

Pareto frontier learning with expensive correlated objectives

Shah, A and Ghahramani, Z (2016) Pareto frontier learning with expensive correlated objectives. In: UNSPECIFIED pp. 2839-2849..

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© 2016 by the author(s). There has been a surge of research interest in developing tools and analysis for Bayesian optimization, the task of finding the global maximizer of an unknown, expensive function through sequential evaluation using Bayesian decision theory. However, many interesting problems involve optimizing multiple, expensive to evaluate objectives simultaneously, and relatively little research has addressed this setting from a Bayesian theoretic standpoint. A prevailing choice when tackling this problem, is to model the multiple objectives as being independent, typically for ease of computation. In practice, objectives are correlated to some extent. In this work, we incorporate the modelling of intertask correlations, developing an approximation to overcome intractable integrals. We illustrate the power of modelling dependencies between objectives on a range of synthetic and real world multi-objective optimization problems.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:23
Last Modified: 19 Jul 2018 07:26