Graph Random Features for Scalable Gaussian Processes
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915514196951040 |
|---|---|
| author | Zhang, Matthew Lin, Jihao Andreas Choromanski, Krzysztof Weller, Adrian Turner, Richard E. Reid, Isaac |
| author_facet | Zhang, Matthew Lin, Jihao Andreas Choromanski, Krzysztof Weller, Adrian Turner, Richard E. Reid, Isaac |
| contents | We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input spaces. We prove that (under mild assumptions) Bayesian inference with GRFs enjoys $O(N^{3/2})$ time complexity with respect to the number of nodes $N$, compared to $O(N^3)$ for exact kernels. Substantial wall-clock speedups and memory savings unlock Bayesian optimisation on graphs with over $10^6$ nodes on a single computer chip, whilst preserving competitive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Random Features for Scalable Gaussian Processes Zhang, Matthew Lin, Jihao Andreas Choromanski, Krzysztof Weller, Adrian Turner, Richard E. Reid, Isaac Machine Learning We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input spaces. We prove that (under mild assumptions) Bayesian inference with GRFs enjoys $O(N^{3/2})$ time complexity with respect to the number of nodes $N$, compared to $O(N^3)$ for exact kernels. Substantial wall-clock speedups and memory savings unlock Bayesian optimisation on graphs with over $10^6$ nodes on a single computer chip, whilst preserving competitive performance. |
| title | Graph Random Features for Scalable Gaussian Processes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.03691 |