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

arXiv: https://arxiv.org/abs/2606.06742 · HTML: https://mlsanity.com/paper/2606.06742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [Sparse Distance Weighted Discrimination](https://mlsanity.com/paper/1501.06066/) (2015)
- [fastkqr: A Fast Algorithm for Kernel Quantile Regression](https://mlsanity.com/paper/2408.05393/) (2024)

## Abstract (arXiv)

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.