Efficient crowdsourcing of unknown experts using bounded multi-armed bandits
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12485
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2014.04.005
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 89
- Volume
- 214
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2014
- 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
-
3
- Research group(s)
-
I - Artificial Intelligence and Human-Centred Computing
- Citation count
- 56
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly cited paper was published in the top journal in artificial intelligence. It led to a number of new lines of research, including: new solutions for budgeted task allocation problems in crowdsourcing systems (Pan, IJCAI 2016; Rangi, AAMAS 2018), new theoretical models for constrained online machine learning (Heidari, IJCAI 2016; Levine, NeurIPS 2017; Jain, Artificial Intelligence 2018), and new applications to online keyword bidding (Gatti, Artificial Intelligence 2015). It was also key to Tran-Thanh being an invited speaker at the IJCAI 2019 Early Career Spotlight Talks.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -