Quinn et al propose challenge datasets in their work called ``Kryptonite-N". These datasets aim to counter the universal function approximation argument of machine learning, breaking the notation that machine learning can ``approximate any continuous function" \cite{original_paper}. Our work refutes this claim and shows that universal function approximations can be applied successfully; the Kryptonite datasets are constructed predictably, allowing logistic regression with sufficient polynomial expansion and L1 regularization to solve for any dimension N.
Cite
@article{arxiv.2412.20588,
title = {Kryptonite-N: Machine Learning Strikes Back},
author = {Albus Li and Nathan Bailey and Will Sumerfield and Kira Kim},
journal= {arXiv preprint arXiv:2412.20588},
year = {2025}
}