Scalable Gaussian process inference via neural feature maps
We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels. We show that the learned feature map can be interpreted as an optimal low-rank approximation to a Gram matrix derived from an implied RKHS, from which we establish consistency of the GP posterior. We further analyse the spectral properties of the induced kernels and introduce product feature-map kernels to address oversmoothing. This simple yet powerful approach enables fast, scalable, and accurate exact GP inference with minimal upfront work. The flexibility of kernel design supports seamless application to both regression and classification tasks across dive...
| Authors | Anthony Stephenson |
|---|---|
| Primary category | stat.ML |
| Published | 11 May 2026 |
| Last updated at source | 24 July 2026 |
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