Random Fourier Signature Features

1 code implementation • 20 Nov 2023 • Csaba Toth, Harald Oberhauser, Zoltan Szabo

Tensor algebras give rise to one of the most powerful measures of similarity for sequences of arbitrary length called the signature kernel accompanied with attractive theoretical guarantees from stochastic analysis.

Time Series

Capturing Graphs with Hypo-Elliptic Diffusions

1 code implementation • 27 May 2022 • Csaba Toth, Darrick Lee, Celia Hacker, Harald Oberhauser

This results in a novel tensor-valued graph operator, which we call the hypo-elliptic graph Laplacian.

Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections

1 code implementation • ICLR 2021 • Csaba Toth, Patric Bonnier, Harald Oberhauser

Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies.

Imputation Time Series +2

Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

1 code implementation • ICML 2020 • 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.

Gaussian Processes General Classification +3