A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel
Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Rd, Oxford, OX2 6GG
arXiv:2501.07145 [stat.ML], (14 Jan 2025)
@misc{tth2025users,
title={A User’s Guide to $texttt{KSig}$: GPU-Accelerated Computation of the Signature Kernel},
author={Csaba Tóth and Danilo Jr Dela Cruz and Harald Oberhauser},
year={2025},
eprint={2501.07145},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
The signature kernel is a positive definite kernel for sequential and temporal data that has become increasingly popular in machine learning applications due to powerful theoretical guarantees, strong empirical performance, and recently introduced various scalable variations. In this chapter, we give a short introduction to KSig, a Scikit-Learn compatible Python package that implements various GPU-accelerated algorithms for computing signature kernels, and performing downstream learning tasks. We also introduce a new algorithm based on tensor sketches which gives strong performance compared to existing algorithms. The package is available.
January 20, 2025 by hgpu
