Kernel Methods & RKHS Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated August 2026

Key papers on kernel methods and RKHS theory, from classical kernel machines to random features and neural tangent kernels.

Kernel Methods & RKHS: 10 key papers

1. Deep Kernel Learning
   Wilson et al. arXiv 2015.
2. Neural Tangent Kernel: Convergence and Generalization in Neural Networks
   Jacot et al. arXiv 2018.
3. To understand deep learning we need to understand kernel learning
   Belkin et al. arXiv 2018.
4. A Kernel Two-Sample Test
   Gretton et al. Journal of Machine Learning Research 2012.
5. Kernel Mean Embedding of Distributions: A Review and Beyond
   Muandet et al. arXiv 2016.
6. Fastfood: Approximate Kernel Expansions in Loglinear Time
   Le et al. arXiv 2014.
7. Just Interpolate: Kernel "Ridgeless" Regression Can Generalize
   Liang and Rakhlin. arXiv 2018.
8. Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
   Avron et al. arXiv 2018.
9. Towards A Unified Analysis of Random Fourier Features
   Li et al. arXiv 2018.
10. Generalization Properties of Learning with Random Features
    Rudi and Rosasco. arXiv 2016.

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