Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1919-1927, 2016.
Abstract
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 inter-task 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.
@InProceedings{pmlr-v48-shahc16,
title = {Pareto Frontier Learning with Expensive Correlated Objectives},
author = {Amar Shah and Zoubin Ghahramani},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {1919--1927},
year = {2016},
editor = {Maria Florina Balcan and Kilian Q. Weinberger},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/shahc16.pdf},
url = {http://proceedings.mlr.press/v48/shahc16.html},
abstract = {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 inter-task 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.}
}
%0 Conference Paper
%T Pareto Frontier Learning with Expensive Correlated Objectives
%A Amar Shah
%A Zoubin Ghahramani
%B Proceedings of The 33rd International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2016
%E Maria Florina Balcan
%E Kilian Q. Weinberger
%F pmlr-v48-shahc16
%I PMLR
%J Proceedings of Machine Learning Research
%P 1919--1927
%U http://proceedings.mlr.press
%V 48
%W PMLR
%X 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 inter-task 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.
TY - CPAPER
TI - Pareto Frontier Learning with Expensive Correlated Objectives
AU - Amar Shah
AU - Zoubin Ghahramani
BT - Proceedings of The 33rd International Conference on Machine Learning
PY - 2016/06/11
DA - 2016/06/11
ED - Maria Florina Balcan
ED - Kilian Q. Weinberger
ID - pmlr-v48-shahc16
PB - PMLR
SP - 1919
DP - PMLR
EP - 1927
L1 - http://proceedings.mlr.press/v48/shahc16.pdf
UR - http://proceedings.mlr.press/v48/shahc16.html
AB - 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 inter-task 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.
ER -
Shah, A. & Ghahramani, Z.. (2016). Pareto Frontier Learning with Expensive Correlated Objectives. Proceedings of The 33rd International Conference on Machine Learning, in PMLR 48:1919-1927
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