Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators
In this work, we investigate the generalization properties of random feature methods. Our analysis extends prior results for Tikhonov regularization to a broad class of spectral regularization techniques and further generalizes the setting to operator-valued kernels. This unified framework enables a rigorous theoretical analysis of neural operators and neural networks through the lens of the Neural Tangent Kernel (NTK). In particular, it allows us to establish optimal learning rates and provides a good understanding of how many neurons are required to achieve a given accuracy. Furthermore, we establish minimax rates in the well-specified case and also in the misspecified case, where the t...
| Authors | Mike Nguyen, Nicole Mücke |
|---|---|
| Primary category | stat.ML |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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