A User's Guide to $\texttt{KSig}$: GPU-Accelerated Computation of the Signature Kernel
This work provides a practical tool for researchers and practitioners in machine learning to efficiently handle signature kernel computations, though it is incremental as it builds on prior scalable variations.
The authors introduced KSig, a GPU-accelerated Python package for computing signature kernels on sequential data, and developed a new tensor sketch-based algorithm that offers strong performance improvements over existing methods.
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 $\texttt{KSig}$, a $\texttt{Scikit-Learn}$ 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.