Constructing Gaussian Processes via Samplets

Marcel Neugebauer

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arXiv · 2411.07277

Constructing Gaussian Processes via Samplets

Marcel Neugebauer

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Abstract

Gaussian Processes face two primary challenges: constructing models for large datasets and selecting the optimal model. This master's thesis tackles these challenges in the low-dimensional case. We examine recent convergence results to identify models with optimal convergence rates and pinpoint essential parameters. Utilizing this model, we propose a Samplet-based approach to efficiently construct and train the Gaussian Processes, reducing the cubic computational complexity to a log-linear scale. This method facilitates optimal regression while maintaining efficient performance.

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BibTeX RIS

Marcel Neugebauer. 2024-11-11. Constructing Gaussian Processes via Samplets. https://arxiv.org/abs/2411.07277

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