Constructing Gaussian Processes via Samplets
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910695087407104 |
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| author | Neugebauer, Marcel |
| author_facet | Neugebauer, Marcel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07277 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Constructing Gaussian Processes via Samplets Neugebauer, Marcel Machine Learning Numerical Analysis 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. |
| title | Constructing Gaussian Processes via Samplets |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2411.07277 |