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

arXiv CS Monday 08 June 2026, 04:00 UTC By Yikai Zhang, Gaoxiang Jia, Jie Ding, Boxiang Wang 1 min read

Key Points

Announce Type: new 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 together with substantial...

arXiv:2606.06742v1 Announce Type: new 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 together with substantial speedups over standard baselines. Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.

GPU (ORG) API (ORG)

Originally published by arXiv CS Read original →

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

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