Distributed Gaussian Processes With Uncertain Inputs
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DOI: 10.1109/access.2024.3501409
Abstract: Gaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods offer scalable solutions by dividing data among multiple GP models (or “experts”). DGPs have… read more here.
Keywords: processes uncertain; distributed gaussian; gaussian process; uncertain inputs ... See more keywords