Wahkon: A Statistically Principled Deep RKHS Superposition Network
For practitioners needing both high accuracy and statistical guarantees (e.g., uncertainty quantification), Wahkon provides a principled deep learning framework with finite-sample guarantees.
Wahkon proposes a deep RKHS superposition network that unifies Kolmogorov's superposition principle with RKHS regularization, achieving minimax-optimal convergence rates and outperforming MLPs, NTKs, and KANs on benchmarks and a single-cell CITE-seq study.
Deep learning excels at prediction but often lacks finite-sample guarantees and calibrated uncertainty; RKHS (Reproducing Kernel Hilbert Space)-based methods provide those guarantees but struggle to adapt in high dimensions. We propose Wahkon, a deep RKHS superposition network that unifies Kolmogorov's superposition principle with RKHS regularization in the smoothing-spline tradition of Wahba. This yields a finite-dimensional deep representer theorem that makes training tractable and provides explicit layerwise complexity control. We show the penalized estimator is exactly the MAP (maximum a posteriori) estimate under a hierarchical Gaussian-process prior, extending the spline/GP duality to deep compositions. Using metric-entropy arguments, we establish minimax-optimal convergence rates under mild smoothness and clarify how depth and width trade off with regularity. Empirically, Wahkon outperforms multilayer perceptrons, Neural Tangent Kernels, and Kolmogorov--Arnold Networks across simulation benchmarks and a single-cell CITE-seq study. By unifying Kolmogorov's superposition principle with RKHS regularization, Wahkon delivers accuracy, interpretability, and statistical rigor in a single framework.