3 papers
stat.ML2025
Scalable Machine Learning Algorithms using Path Signatures
Csaba Tóth
The interface between stochastic analysis and machine learning is a rapidly evolving field, with path signatures - iterated integrals that provide faithful, hierarchical representa…
cs.LG2022
Capturing Graphs with Hypo-Elliptic Diffusions
Csaba Toth, Darrick Lee, Celia Hacker +1
Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through ra…
stat.ML2019
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth, Harald Oberhauser
We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions. This allows to make sequ…