Constructing Gaussian Processes via Samplets

• URL: http://arxiv.org/abs/2411.07277v1
• Date: Mon, 11 Nov 2024 18:01:03 GMT
• Title: Constructing Gaussian Processes via Samplets
• Authors: Marcel Neugebauer,
• Abstract summary: We examine recent convergence results to identify models with optimal convergence rates. We propose a Samplet-based approach to efficiently construct and train the Gaussian Processes.
• Score: 0.0
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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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