TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
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Computer Science > Machine Learning
Title:TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
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 this https URL, and the package can be easily installed via PyPI.
| Comments: | 14 pages, 2 figures |
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2606.06742 [cs.LG] |
| (or arXiv:2606.06742v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06742
arXiv-issued DOI via DataCite (pending registration)
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