# A User's Guide to textttKSig: GPU-Accelerated Computation of the Signature Kernel

arXiv: https://arxiv.org/abs/2501.07145 · HTML: https://mlsanity.com/paper/2501.07145/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Kernel Learning for Mean-Variance Trading Strategies](https://mlsanity.com/paper/2507.10701/) (2025)
- [Learning Manifold and Ito Dynamics with Branched Neural Rough Differential Equations](https://mlsanity.com/paper/2606.05272/) (2026)

## Abstract (arXiv)

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 textttKSig, a textttScikit-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.