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Variational Elliptical Processes

Transactions on Machine Learning Research, 2023 [Peer Reviewed Journal]

ISSN: 2835-8856

Digital Resources/Online E-Resources

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  • Title:
    Variational Elliptical Processes
  • Author: Bånkestad, Maria ; Sjölund, Jens ; Taghia, Jalil ; Schön, Thomas B.
  • Subjects: Artificial Intelligence ; Artificiell intelligens ; Machine learning ; Maskininlärning
  • Is Part Of: Transactions on Machine Learning Research, 2023
  • Description: We present elliptical processes—a family of non-parametric probabilistic models that subsumes Gaussian processes and Student's t processes. This generalization includes a range of new heavy-tailed behaviors while retaining computational tractability. Elliptical processes are based on a representation of elliptical distributions as a continuous mixture of Gaussian distributions. We parameterize this mixture distribution as a spline normalizing flow, which we train using variational inference. The proposed form of the variational posterior enables a sparse variational elliptical process applicable to large-scale problems. We highlight advantages compared to Gaussian processes through regression and classification experiments. Elliptical processes can supersede Gaussian processes in several settings, including cases where the likelihood is non-Gaussian or when accurate tail modeling is essential.
  • Language: English
  • Identifier: ISSN: 2835-8856
  • Source: SWEPUB Freely available online
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