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Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs


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Publication Date: 2015

Journal Title: Journal of Machine Learning Research

Publisher: Microtome Publishing

Pages: 655-664

Language: English

Type: Article

Metadata: Show full item record

Citation: Gal, Y., & Turner, R. (2015). Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs. Journal of Machine Learning Research, 655-664. http://jmlr.org/proceedings/papers/v37/galb15.html

Description: This is the final version of the article. It first appeared at http://jmlr.org/proceedings/papers/v37/galb15.html

Abstract: Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance function with a finite Fourier series approximation and treat it as a random variable. The random covariance function has a posterior, on which a variational distribution is placed. The variational distribution transforms the random covariance function to fit the data. We study the properties of our approximate inference, compare it to alternative ones, and extend it to the distributed and stochastic domains. Our approximation captures complex functions better than standard approaches and avoids over-fitting.

Sponsorship: YG is supported by the Google European Fellowship in Machine Learning. Funding was provided by the EPSRC (grant numbers EP/G050821/1 and EP/L000776/1) and Google (R.E.T.).

Identifiers:

External link: http://jmlr.org/proceedings/papers/v37/galb15.html

This record's URL: https://www.repository.cam.ac.uk/handle/1810/250392

Rights: Attribution-NonCommercial 2.0 UK: England & Wales

Licence URL: http://creativecommons.org/licenses/by-nc/2.0/uk/





Autor: Gal, YarinTurner, Richard

Fuente: https://www.repository.cam.ac.uk/handle/1810/250392



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