Bayesian optimization using sequential Monte Carlo

Abstract : We consider the problem of optimizing a real-valued continuous function $f$ using a Bayesian approach, where the evaluations of $f$ are chosen sequentially by combining prior information about $f$, which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are used to choose evaluation points. In this article, we decide to use a Sequential Monte Carlo (SMC) approach.
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Communication dans un congrès
Youssef Hamadi and Marc Schoenauer. 6th International Conference on Learning and Intelligent Optimization (LION6), Jan 2012, Paris, France. Springer Berlin Heidelberg, pp.339-342, 2012, Lecture Notes in Computer Science. 〈10.1007/978-3-642-34413-8_24〉
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Contributeur : Julien Bect <>
Soumis le : jeudi 12 juillet 2012 - 11:11:54
Dernière modification le : jeudi 29 mars 2018 - 11:06:05
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Romain Benassi, Julien Bect, Emmanuel Vazquez. Bayesian optimization using sequential Monte Carlo. Youssef Hamadi and Marc Schoenauer. 6th International Conference on Learning and Intelligent Optimization (LION6), Jan 2012, Paris, France. Springer Berlin Heidelberg, pp.339-342, 2012, Lecture Notes in Computer Science. 〈10.1007/978-3-642-34413-8_24〉. 〈hal-00717195〉

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