Heterogeneous Multi-output Gaussian Process Prediction
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5207
- Type
- E - Conference contribution
- DOI
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-
- Title of conference / published proceedings
- Advances in Neural Information Processing Systems 32 (NeurIPS 2018)
- First page
- 6711
- Volume
- 32
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Access exception
- Month of publication
- December
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- 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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C - Machine Learning
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces the first probabilistic, scalable multi-task learning approach that allows for tasks to have different nature, e.g. combinations of count data, continuous, binary and categorical. It received a spotlight at NeurIPS 2018 (acceptance rate = 3%), and has inspired further publications (https://arxiv.org/abs/1911.00002, https://arxiv.org/abs/1911.10225). The model is currently used in the funded grant Innovate UK Reference 104316.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -