We study the inference of mean-variance robustness measures to quantify input uncertainty under the Gaussian Process (GP) framework. These measures are widely ...

We study the inference of mean-variance robust- ness measures to quantify input uncertainty under the Gaussian Process (GP) framework. These measures are widely ...

Optimize black box function f. • Consider Input Uncertainty. • What is a robust solution? • First moment: J𝜉. f = E𝜉. f x + 𝜉. • Second moment: V𝜉. f = V𝜉.

Jul 26, 2022 · In this paper, we propose a Spectral Representation of Robustness Measures based on the GP's spectral representation, i.e., an analytical ...

This repository contains the code and experiments done in the work "Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty"

Spectral representation of robustness measures for optimization under input uncertainty. J Qing, T Dhaene, I Couckuyt. 38th International Conference on Machine ...

Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty. Jixiang Qing, Tom Dhaene, Ivo Couckuyt. International Conference on ...

Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty · Jixiang Qing, Tom Dhaene, Ivo Couckuyt. Published: 31 Dec 2021, Last ...

We study the inference of mean-variance robust-ness measures to quantify input uncertainty under the Gaussian Process (GP) framework.

Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty · Jixiang QingT. DhaeneI. Couckuyt. Engineering, Mathematics. ICML. 2022.