Jul 12, 2024 · Abstract: This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration.

Oct 10, 2023 · The Gaussian process (GP) is an efficient nonparametric machine learning (ML) method. A distinct advantage of the GP is its ability to provide an estimate ...

Mar 5, 2024 · Metric learning is about representing your data objects, such as images or text or whatever, with numerical vectors such that the data objects that are ...

Feb 5, 2024 · This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs).

Feb 22, 2024 · This paper focuses on the estimation of the Gaussian process covariance parameters by reviewing recent works on the analysis of the advantages and disadvantages ...

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Nov 24, 2023 · State-of-the-art approaches for designing calibrated models rely on inflating the Gaussian process posterior variance, which yields confidence intervals that ...

Oct 31, 2023 · In this paper, we propose the multi-view collaborative Gaussian process dynamical systems. (McGPDSs) model, which assumes that the private latent variable for ...

Jan 19, 2024 · We present a linear, Gaussian-process piecewise Bayesian approach to fit a spherical solar wind of time-variable amplitude, which has been implemented in the ...

Jan 15, 2024 · In this study, feature acquisition and selection are proposed to prepare input data for a simulation-based learning approach.