Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
نویسندگان
چکیده
منابع مشابه
Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints
This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. PESMOC can hence be used to solve a wide range of optimization problems. Iteratively, PESMOC chooses an input location on w...
متن کاملPredictive Entropy Search for Multi-objective Bayesian Optimization
We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. The central idea of PESMO is to choose evaluation points so as to maximally reduce the entropy of the posterior distribution over the Pareto set. Critically, the PESMO multi-objective acquisition function can be decomposed as a sum of objecti...
متن کاملPredictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic. However, EI can lead to pathologies when used with constraints. For example, in the case of decoupled constraints—i.e., when one can independently evaluate the ob...
متن کاملSupplementary Material for: Predictive Entropy Search for Multi-objective Bayesian Optimization
In this section we describe in detail the specific steps of the EP algorithm that is required for the evaluation of the proposed acquisition function, PESMO. More precisely, we show how to compute the EP approximation to the conditional predictive distribution of each objective fk. From the main manuscript we know that that this distribution is obtained by multiplying the GP posteriors by the p...
متن کاملPredictive Entropy Search for Bayesian Optimization with Unknown Constraints Supplementary Material
PESC computes a Gaussian approximation to the NFCPD (main text, Eq. (11)) using Expectation Propagation (EP) (Minka, 2001). EP is a method for approximating a product of factors (often a single prior factor and multiple likelihood factors) with a tractable distribution, for example a Gaussian. EP generates a Gaussian approximation by approximating each individual factor with a Gaussian. The pro...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2019
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.06.025