A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
- Submitting institution
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University of Cambridge
- Unit of assessment
- 12 - Engineering
- Output identifier
- 7791
- Type
- D - Journal article
- DOI
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- Title of journal
- Journal of Machine Learning Research
- Article number
- -
- First page
- 1
- Volume
- 18
- Issue
- 104
- ISSN
- 1532-4435
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper led to invited tutorials at the Machine Learning Summer School in Madrid 2018 (http://mlss.ii.uam.es/mlss2018/speakers.html) and the Imperial College London Machine Learning Tutorial Series in 2016 (the video tutorial of which has >35,800 views as of 15/06/20, https://www.youtube.com/watch?v=92-98SYOdlY). The methods in the paper have been accepted for inclusion into the open source Gaussian Process software library GPML (implemented in the function: http://www.gaussianprocess.org/gpml/code/matlab/cov/apxSparse.m).
- Author contribution statement
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- Non-English
- No
- English abstract
- -