Adaptive RKHS Fourier Features for Compositional Gaussian Process Models Article Swipe
Xinxing Shi
,
Thomas Baldwin-McDonald
,
Mauricio A. Álvarez
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.01856
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.01856
Deep Gaussian Processes (DGPs) leverage a compositional structure to model non-stationary processes. DGPs typically rely on local inducing point approximations across intermediate GP layers. Recent advances in DGP inference have shown that incorporating global Fourier features from the Reproducing Kernel Hilbert Space (RKHS) can enhance the DGPs' capability to capture complex non-stationary patterns. This paper extends the use of these features to compositional GPs involving linear transformations. In particular, we introduce Ordinary Differential Equation(ODE)--based RKHS Fourier features that allow for adaptive amplitude and phase modulation through convolution operations. This convolutional formulation relates our work to recently proposed deep latent force models, a multi-layer structure designed for modelling nonlinear dynamical systems. By embedding these adjustable RKHS Fourier features within a doubly stochastic variational inference framework, our model exhibits improved predictive performance across various regression tasks.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.01856
- https://arxiv.org/pdf/2407.01856
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400373892
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400373892Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.01856Digital Object Identifier
- Title
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Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-01Full publication date if available
- Authors
-
Xinxing Shi, Thomas Baldwin-McDonald, Mauricio A. ÁlvarezList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.01856Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.01856Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.01856Direct OA link when available
- Concepts
-
Reproducing kernel Hilbert space, Fourier transform, Gaussian process, Process (computing), Mathematics, Computer science, Gaussian, Pure mathematics, Mathematical analysis, Physics, Hilbert space, Operating system, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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