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

• URL: http://arxiv.org/abs/2606.06742v2
• Date: Tue, 09 Jun 2026 00:36:29 GMT
• Title: TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
• Abstract summary: TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression.<n>The library features a scikit-learn-style API and is designed to exploit GPU-friendly linear algebra.<n>Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.
• Score: 10.963977296401582
• License: http://creativecommons.org/licenses/by/4.0/
• 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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