Efficient Global Optimization using Deep Gaussian Processes
Efficient Global Optimization (EGO) is widely used for the optimization of computationally expensive black-box functions. It uses a surrogate modeling technique based on Gaussian Processes (Kriging). However, due to the use of a stationary covariance, Kriging is not well suited for approximating non stationary functions. This paper explores the integration of Deep Gaussian processes (DGP) in EGO framework to deal with the non-stationary issues and investigates the induced challenges and opportunities. Numerical experimentations are performed on analytical problems to highlight the different aspects of DGP and EGO.
Code (0)
등록된 구현이 없습니다.
Tasks
Gaussian Processesglobal-optimizationSimilar Papers 제목 키워드 기반
Global Optimization of Gaussian processes
Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in…
Bayesian OptimizationGaussian Processesglobal-optimizationBayesian Optimization using Deep Gaussian Processes
Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classi…
Bayesian OptimizationGaussian Processesglobal-optimizationFinancial Applications of Gaussian Processes and Bayesian Optimization
In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gau…
Bayesian OptimizationBIG-bench Machine LearningGaussian Processesglobal-optimizationOptimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-di…
Gaussian ProcessesDistributed Quantum Gaussian Processes for Multi-Agent Systems
Gaussian Processes (GPs) are a powerful tool for probabilistic modeling, but their performance is often constrained in complex, large-scale real-world domains due to the limited expressivity of classical kernels. Quantum…
Distributed OptimizationGaussian Processes