BOAT: Building auto-tuners with structured Bayesian optimization
- Submitting institution
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University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1875
- Type
- E - Conference contribution
- DOI
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10.1145/3038912.3052662
- Title of conference / published proceedings
- 26th International World Wide Web Conference, WWW 2017
- First page
- 479
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- January
- Year of publication
- 2017
- 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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-
- Citation count
- 20
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- BOAT is based on Structural Bayesian Optimization to leverage contextual information in the form of a probabilistic model of systems behaviour, with Probabilistic Programming support to build such a model. BOAT addresses a key problem for Bayesian optimisation, when the probabilistic model using Gaussian process fails to accurately capture the objective function landscape if the model contains too many dimensions. The paper was featured in the well-known blog the Morning Paper. The PhD student who worked on the core part of the project was nominated for the British Computer Science PhD dissertation award.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -