Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21228 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912605064396800 |
|---|---|
| author | Wilson, Andrew Gordon Hu, Zhiting Salakhutdinov, Ruslan Xing, Eric P. |
| author_facet | Wilson, Andrew Gordon Hu, Zhiting Salakhutdinov, Ruslan Xing, Eric P. |
| contents | This note responds to "Promises and Pitfalls of Deep Kernel Learning" (Ober et al., 2021). The marginal likelihood of a Gaussian process can be compartmentalized into a data fit term and a complexity penalty. Ober et al. (2021) shows that if a kernel can be multiplied by a signal variance coefficient, then reparametrizing and substituting in the maximized value of this parameter sets a reparametrized data fit term to a fixed value. They use this finding to argue that the complexity penalty, a log determinant of the kernel matrix, then dominates in determining the other values of kernel hyperparameters, which can lead to data overcorrelation. By contrast, we show that the reparametrization in fact introduces another data-fit term which influences all other kernel hyperparameters. Thus, a balance between data fit and complexity still plays a significant role in determining kernel hyperparameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Response to Promises and Pitfalls of Deep Kernel Learning Wilson, Andrew Gordon Hu, Zhiting Salakhutdinov, Ruslan Xing, Eric P. Machine Learning This note responds to "Promises and Pitfalls of Deep Kernel Learning" (Ober et al., 2021). The marginal likelihood of a Gaussian process can be compartmentalized into a data fit term and a complexity penalty. Ober et al. (2021) shows that if a kernel can be multiplied by a signal variance coefficient, then reparametrizing and substituting in the maximized value of this parameter sets a reparametrized data fit term to a fixed value. They use this finding to argue that the complexity penalty, a log determinant of the kernel matrix, then dominates in determining the other values of kernel hyperparameters, which can lead to data overcorrelation. By contrast, we show that the reparametrization in fact introduces another data-fit term which influences all other kernel hyperparameters. Thus, a balance between data fit and complexity still plays a significant role in determining kernel hyperparameters. |
| title | Response to Promises and Pitfalls of Deep Kernel Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.21228 |