# Automated Spectral Kernel Learning

arXiv: https://arxiv.org/abs/1909.04894 · HTML: https://mlsanity.com/paper/1909.04894/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Operation-level Progressive Differentiable Architecture Search](https://mlsanity.com/paper/2302.05632/) (2023)
- [Ridgeless Regression with Random Features](https://mlsanity.com/paper/2205.00477/) (2022)
- [RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features](https://mlsanity.com/paper/2211.06410/) (2022)

## Abstract (arXiv)

The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits their applications on complicated tasks. In this paper, we propose a powerful and efficient spectral kernel learning framework and learned kernels are dependent on both inputs and outputs, by using non-stationary spectral kernels and flexibly learning the spectral measure from the data. Further, we derive a data-dependent generalization error bound based on Rademacher complexity, which estimates the generalization ability of the learning framework and suggests two regularization terms to improve performance. Extensive experimental results validate the effectiveness of the proposed algorithm and confirm our theoretical results.