Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2494
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- Advances in Neural Information Processing Systems 30 (NIPS 2017)
- First page
- 5131
- Volume
- -
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2017
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- 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
- Our paper is the first to exploit dependencies between stochastic processes to increase model performance when a process unknown at training time appears. Our technique scales well with large datasets. Published in a leading machine learning conference, the work has been influential in other machine learning areas such as reinforcement learning (Sæmundsson et al., 2018 https://arxiv.org/pdf/1803.07551.pdf) and meta Bayesian optimisation (Klein et al., Neurips 2019 https://papers.nips.cc/paper/2019/file/0668e20b3c9e9185b04b3d2a9dc8fa2d-Paper.pdf; Atkinson, AIAA 2020 doi.org/10.2514/6.2020-1145). The research underpinning this paper was funded by the EPSRC grant EP/N014162/1 and the model proposed has been used in the Rosetrees Trust funded grant (ref: A2501).
- Author contribution statement
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- Non-English
- No
- English abstract
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