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arXiv · 2606.06742

TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection

Yikai Zhang·Gaoxiang Jia·Jie Ding·Boxiang Wang
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Abstract

TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration. The library features a scikit-learn-style API and is designed to exploit GPU-friendly linear algebra, accelerating the full training and model-selection pipeline through intelligent reuse of matrix operations. Benchmarks show competitive predictive performance with substantial speedups over standard baselines. The efficiency and programmable design also make TorchKM a kernel-learning component for AI-driven workflows. Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.

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Yikai Zhang, Gaoxiang Jia, Jie Ding, Boxiang Wang. 2026-06-04. TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection. https://arxiv.org/abs/2606.06742

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