A User’s Guide to KSig: GPU-Accelerated Computation of the Signature Kernel

Csaba Tóth (), Danilo Jr Dela Cruz () and Harald Oberhauser ()
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Csaba Tóth: University of Oxford, Mathematical Institute
Danilo Jr Dela Cruz: University of Oxford, Mathematical Institute
Harald Oberhauser: University of Oxford, Mathematical Institute

A chapter in Stochastic Analysis and Applications 2025, 2026, pp 477-523 from Springer

Abstract: Abstract 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 multiple recently introduced 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 at https://github.com/tgcsaba/ksig .

Date: 2026
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DOI: 10.1007/978-3-032-03914-9_16

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