AxlePro: Momentum-Accelerated Batched Training of Kernel Machines
Abstract
In this paper we derive a novel iterative algorithm for learning kernel machines. Our algorithm, $\textsf{AxlePro}$, extends the $\textsf{EigenPro}$ family of algorithms via momentum-based acceleration. $\textsf{AxlePro}$ can be applied to train kernel machines with arbitrary positive semidefinite kernels. We provide a convergence guarantee for the algorithm and demonstrate the speed-up of $\textsf{AxlePro}$ over competing algorithms via numerical experiments. Furthermore, we also derive a version of $\textsf{AxlePro}$ to train large kernel models over arbitrarily large datasets.
Cite
Text
Zhang and Pandit. "AxlePro: Momentum-Accelerated Batched Training of Kernel Machines." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Zhang and Pandit. "AxlePro: Momentum-Accelerated Batched Training of Kernel Machines." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/zhang2025aistats-axlepro/)BibTeX
@inproceedings{zhang2025aistats-axlepro,
title = {{AxlePro: Momentum-Accelerated Batched Training of Kernel Machines}},
author = {Zhang, Yiming and Pandit, Parthe},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {1666-1674},
volume = {258},
url = {https://mlanthology.org/aistats/2025/zhang2025aistats-axlepro/}
}