Highly Adaptive Ridge
<|reference_start|>Highly Adaptive Ridge: In this paper we propose the Highly Adaptive Ridge (HAR): a regression method that achieves a $n^{-1/3}$ dimension-free L2 convergence rate in the class of right-continuous functions with square-integrable sectional derivatives. This is a large nonparametric function class that is particularly appropriate for tabular data. HAR is exactly kernel ridge regression with a specific data-adaptive kernel based on a saturated zero-order tensor-product spline basis expansion. We use simulation and real data to confirm our theory. We demonstrate empirical performance better than state-of-the-art algorithms for small datasets in particular.<|reference_end|>
@article{schuler2024highly,
    title={Highly Adaptive Ridge},
    author={Alejandro Schuler, Alexander Hagemeister, Mark van der Laan},
    journal={arXiv preprint arXiv:2410.02680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02680},
    primaryClass={stat.ML cs.LG}
}