Mind the nuisance: Gaussian process classification using privileged noise
- Submitting institution
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University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 335583_54603
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- Proceedings of Advances in Neural Information Processing Systems 27 (NIPS 2014); Palais des Congrès de Montréal, Montréal Canada; 8 - 13 December 2014
- First page
- 837
- Volume
- -
- Issue
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- ISSN
- -
- Open access status
- -
- Month of publication
- January
- Year of publication
- 2014
- URL
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https://proceedings.neurips.cc/paper/2014/hash/6e2713a6efee97bacb63e52c54f0ada0-Abstract.html
- 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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4
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper proposed the first Bayesian treatment of privileged learning (LUPI) using a Gaussian process prior and showed its advantages over non-Bayesian LUPI in terms of running time and predictive performance. A follow-up work led to funding by an EPSRC First Grant [1], and by the British Academy [2]. Based on the series of works on LUPI, Quadrianto is regularly serving as an Area Chair in the Neural Information Processing Systems Conference since 2015, and as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016.
[1] EthicalML, £100K, https://gtr.ukri.org/projects?ref=EP%2FP03442X%2F1
[2] Inclusive Green Infrastructures, £244K, https://www.thebritishacademy.ac.uk/projects/urban-well-being-inclusive-green-infrastructures/"
- Author contribution statement
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- Non-English
- No
- English abstract
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