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A game theoretic perspective on Bayesian multi-objective optimization

Abstract : This chapter addresses the question of how to efficiently solve many-objective optimization problems in a computationally demanding black-box simulation context. We shall motivate the question by applications in machine learning and engineering, and discuss specific harsh challenges in using classical Pareto approaches when the number of objectives is four or more. Then, we review solutions combining approaches from Bayesian optimization, e.g., with Gaussian processes, and concepts from game theory like Nash equilibria, Kalai-Smorodinsky solutions and detail extensions like Nash-Kalai-Smorodinsky solutions. We finally introduce the corresponding algorithms and provide some illustrating results.
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Preprints, Working Papers, ...
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Contributor : Mickaël Binois Connect in order to contact the contributor
Submitted on : Thursday, April 29, 2021 - 6:16:27 PM
Last modification on : Thursday, March 31, 2022 - 5:14:01 PM
Long-term archiving on: : Friday, July 30, 2021 - 6:02:52 PM


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  • HAL Id : hal-03206174, version 1
  • ARXIV : 2104.14456


Mickael Binois, Abderrahmane Habbal, Victor Picheny. A game theoretic perspective on Bayesian multi-objective optimization. 2021. ⟨hal-03206174⟩



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