Closed-Form Active Learning using Expected Variance Reduction of Gaussian Process Surrogates for Adaptive Sampling | IEEE Conference Publication | IEEE Xplore

Closed-Form Active Learning using Expected Variance Reduction of Gaussian Process Surrogates for Adaptive Sampling

Publisher: IEEE

Abstract:

Adaptive sampling of latent fields remains a challenging task, especially in high-dimensional input spaces. In this paper, we propose an active learning method of expecte...View more

Abstract:

Adaptive sampling of latent fields remains a challenging task, especially in high-dimensional input spaces. In this paper, we propose an active learning method of expected variance reduction with Gaussian process (GP) surrogates using a closed-form gradient. The use of closed-form gradient leads the optimization to find better solutions with reduced computations. We derive the closed-form gradient for active learning Cohn (ALC) using GP surrogates that are formed with the separable squared exponential covariance function. Moreover, we provide algorithmic details for the execution of the closedform ALC (cALC). Numerical experiments with multiple input space dimensions illustrate the efficacy of our method.
Date of Conference: 31 May 2023 - 02 June 2023
Date Added to IEEE Xplore: 03 July 2023
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: San Diego, CA, USA

Funding Agency:


References

References is not available for this document.