Directional Chebyshev harmonics enable spectral path regression for tabular data with closed-form training, competitive accuracy, and explicit interpretability.
Random features for large-scale kernel machines.Advances in neural information processing systems, 20
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Sparse RFNNs with sSVD via Lanczos-Golub-Kahan bidiagonalization maintain accuracy while improving efficiency and robustness for 1D steady convection-diffusion equations with strong advection.
The Neural Basis Method uses a predefined neural basis space and operator residual metric to deliver accurate single solves and fast parametric learning for multiscale Darcian dynamics.
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
A review synthesizing foundations, constructions, advantage conditions, and challenges for non-variational quantum kernel methods in supervised learning.
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A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.