Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
Recent studies have reported $\textit{saturation effects}$ and $\textit{multiple descent behavior}$ in large dimensional kernel ridge regression (KRR). However, these findings are predominantly derived under restrictive settings, such as inner product kernels on sphere or strong eigenfunction assumptions like hypercontractivity. Whether such behaviors hold for other kernels remains an open question. In this paper, we establish a broad, new family of large dimensional kernels and derive the corresponding convergence rates of the generalization error. As a result, we recover key phenomena previously associated with inner product kernels on sphere, including: $i)$ the $\textit{minimax optima...
| Authors | Yang Zhou, Yicheng Li, Yuqian Cheng et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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