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  • title: Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
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            Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
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            Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

            Jul 12, 2020

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            Csaba Toth

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            Harald Oberhauser

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            Über

            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 sequences of different length comparable and to rely on strong theoretical results from stochastic analysis. Signatures capture sequential structure with tensors that can scale unfavourably in sequence length and state space dimension. To deal with this, we introduce a sparse variational approach with inducing tensors. We…

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            I2

            ICML 2020

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            Über ICML 2020

            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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