Add to ORKGWe consider parallel global optimization of derivative-free expensive-to-evaluate functions, and proposes an efficient method based on stochastic approximation for implementing a conceptual Bayesian optimization algorithm proposed by [10]. To accomplish this, we use innitessimal per-turbation analysis (IPA) to construct a stochastic gradient estimator and show that this estimator is unbiased.
International audienceOptimization problems where the objective and constraint functions take minute...
By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergenc...
International audienceBayesian optimization uses a probabilistic model of the objective function to ...
We present a unifying framework for the global optimization of functions which are expensive to eval...
We present a unifying framework for the global optimization of functions which are expensive to eval...
184 pagesNon-convex time-consuming objectives are often optimized using “black-box” optimization. Th...
Abstract. We consider the problem of optimizing a real-valued con-tinuous function f, which is suppo...
International audienceWe consider the problem of optimizing a real-valued continuous function f, whi...
Bayesian optimization, a framework for global optimization of expensive-to-evaluate functions, has r...
Contains fulltext : 83325.pdf (preprint version ) (Open Access
This study addresses the stochastic optimization of a function unknown in closed form which can only...
Revised selected articles from the LION 6 Conference (Paris, Jan. 16-20, 2012), LNCS 7219, 978-3-642...
This paper is concerned with approximating the scalar response of a complex computational model subj...
Bayesian optimization is a powerful global op-timization technique for expensive black-box functions...
Bayesian optimization is a powerful global optimization technique for expensive black-box functions....
International audienceOptimization problems where the objective and constraint functions take minute...
By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergenc...
International audienceBayesian optimization uses a probabilistic model of the objective function to ...
We present a unifying framework for the global optimization of functions which are expensive to eval...
We present a unifying framework for the global optimization of functions which are expensive to eval...
184 pagesNon-convex time-consuming objectives are often optimized using “black-box” optimization. Th...
Abstract. We consider the problem of optimizing a real-valued con-tinuous function f, which is suppo...
International audienceWe consider the problem of optimizing a real-valued continuous function f, whi...
Bayesian optimization, a framework for global optimization of expensive-to-evaluate functions, has r...
Contains fulltext : 83325.pdf (preprint version ) (Open Access
This study addresses the stochastic optimization of a function unknown in closed form which can only...
Revised selected articles from the LION 6 Conference (Paris, Jan. 16-20, 2012), LNCS 7219, 978-3-642...
This paper is concerned with approximating the scalar response of a complex computational model subj...
Bayesian optimization is a powerful global op-timization technique for expensive black-box functions...
Bayesian optimization is a powerful global optimization technique for expensive black-box functions....
International audienceOptimization problems where the objective and constraint functions take minute...
By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergenc...
International audienceBayesian optimization uses a probabilistic model of the objective function to ...