Diverse randomized agents vote to win
- Submitting institution
-
The University of Lancaster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 156631186
- Type
- E - Conference contribution
- DOI
-
-
- Title of conference / published proceedings
- Proceedings of the 28th Neural Information Processing Systems Conference (NIPS 2014)
- First page
- 0
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- December
- Year of publication
- 2014
- URL
-
https://nips.cc/Conferences/2014
- 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
-
5
- Research group(s)
-
B - Data Science
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work, particularly, proves the importance of diversity across voting rules . It has been presented at the leading machine learning conference NeurIPS. Since voting is widely applied (e.g., in Machine Learning), this has great implications to practitioners. It has further inspired additional work by top researchers (e.g., Procaccia, at AAAI and ICML), and it is cited outside Computer Science (e.g., Maleszka in management journal). Additionally, collaborators created RoboVote.org, allowing anyone to apply voting in real life. This work is part of my thesis, which received the "best thesis award" at USC; and helped Procaccia to receive award at IJCAI'2015.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
