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The Signature Kernel

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arxiv 2305.04625 v1 pith:LOQOLVSO submitted 2023-05-08 math.PR cs.LGstat.ML

Darrick Lee , Harald Oberhauser This is my paper · ORCID
classification math.PRcs.LGstat.ML
keywords kernelsignaturetheoreticalalgorithmsanalysiscomputationcomputationaldata
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The signature kernel is a positive definite kernel for sequential data. It inherits theoretical guarantees from stochastic analysis, has efficient algorithms for computation, and shows strong empirical performance. In this short survey paper for a forthcoming Springer handbook, we give an elementary introduction to the signature kernel and highlight these theoretical and computational properties.

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Cited by 1 Pith paper

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  1. Koopman-Equivariant Gaussian Processes

    cs.LG 2025-02 reject novelty 6.0 of 10

    Koopman-equivariant Gaussian processes give a new kernel family for forecasting nonlinear dynamics with closed-form multi-step uncertainty and a claimed sample-complexity reduction.

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