Imperial College London
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ProfessorRuthMisener

Faculty of Engineering, Department of Computing

Professor in Computational Optimisation
 
 
 
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Contact

 

+44 (0)20 7594 8315r.misener Website CV

 
 
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Location

 

379Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Wiebe:2020,
author = {Wiebe, J and Cecílio, I and Dunlop, J and Misener, R},
publisher = {arXiv},
title = {A robust approach to warped Gaussian process-constrained optimization},
url = {http://arxiv.org/abs/2006.08222v1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Optimization problems with uncertain black-box constraints, modeled by warpedGaussian processes, have recently been considered in the Bayesian optimizationsetting. This work introduces a new class of constraints in which the sameblack-box function occurs multiple times evaluated at different domain points.Such constraints are important in applications where, e.g., safety-criticalmeasures are aggregated over multiple time periods. Our approach, which usesrobust optimization, reformulates these uncertain constraints intodeterministic constraints guaranteed to be satisfied with a specifiedprobability, i.e., deterministic approximations to a chance constraint. Thisapproach extends robust optimization methods from parametric uncertainty touncertain functions modeled by warped Gaussian processes. We analyze convexityconditions and propose a custom global optimization strategy for non-convexcases. A case study derived from production planning and an industriallyrelevant example from oil well drilling show that the approach effectivelymitigates uncertainty in the learned curves. For the drill scheduling example,we develop a custom strategy for globally optimizing integer decisions.
AU  - Wiebe,J
AU  - Cecílio,I
AU  - Dunlop,J
AU  - Misener,R
PB  - arXiv
PY  - 2020///
TI  - A robust approach to warped Gaussian process-constrained optimization
UR  - http://arxiv.org/abs/2006.08222v1
UR  - http://hdl.handle.net/10044/1/80017
ER  -
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