Clustered sparse Bayesian learning
- Submitting institution
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University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1852
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015
- First page
- 932
- Volume
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- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- January
- Year of publication
- 2015
- 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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4
- Research group(s)
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-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper results from a collaboration with Dr. David Wipf, (email on request) at Microsoft Research, Beijing. In contrast to probabilistic models previously applied to this problem, our model subscribes to concrete motivating principles that are evaluated theoretically and empirically. It includes analysis of previously unexamined theoretical principles that play a critical role in multi-task sparse estimation , and the development of a robust sparse Bayesian algorithm that adheres to these principles. Our 'semi-Bayesian' strategy promotes understanding of the central mechanisms at work in producing a successful algorithm, including all approximations involved, while achieving then state-of-the-art multi-task sparse estimation performance.
- Author contribution statement
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- Non-English
- No
- English abstract
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