A maximum likelihood approach for selecting sets of alternatives

Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

Volumn , Issue , 2012, Pages 695-704

  Procaccia, Ariel D ^a   Reddi, Sashank J ^a   Shah, Nisarg ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MAXIMUM LIKELIHOOD APPROACHES; MAXIMUM LIKELIHOOD ESTIMATE; NOISE LEVELS; NP-HARD; OPTIMAL SOLUTIONS; RANKING OF ALTERNATIVES; RELATIVE STRENGTH;

MAXIMUM LIKELIHOOD ESTIMATION;

ARTIFICIAL INTELLIGENCE;

EID: 84879753257     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. N. Ailon, M. Charikar, and A. Newman. Aggregating inconsistent information: Ranking and clustering. In Proceedings of the 37th Annual ACM Symposium on Theory of Computing (STOC), pages 684-693, 2005.
2. N. Alon. Ranking tournaments. SIAM Journal of Discrete Mathematics, 20(1-2):137-142, 2006.
3. J. Bartholdi, C. A. Tovey, and M. A. Trick. Voting schemes for which it can be difficult to tell who won the election. Social Choice and Welfare, 6:157-165, 1989.
4. M. Braverman and E. Mossel. Noisy sorting without resampling. In Proceedings of the 19th Annual ACMSIAM Symposium on Discrete Algorithms (SODA), pages 268-276, 2008.
5. P. Charbit, S. Thomasse, and A. Yeo. The minimum feedback arc set problem is NP-hard for tournaments. Combinatorics, Probability &Computing, 16(1):1-4, 2007.
6. V. Conitzer and T. Sandholm. Common voting rules as maximum likelihood estimators. In Proceedings of the 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI), pages 145-152, 2005.
7. V. Conitzer, A. Davenport, and H. Kalagnanam. Improved bounds for computing Kemeny rankings. In Proceedings of the 21st AAAI Conference on Artificial Intelligence (AAAI), pages 620-626, 2006.
8. V. Conitzer, M. Rognlie, and L. Xia. Preference functions that score rankings and maximum likelihood estimation. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI), pages 109-115, 2009.
9. S. Cooper, F. Khatib, A. Treuille, J. Barbero, J. Lee, M. Beenen, A. Leaver-Fay, D. Baker, and Z. Popovic. Predicting protein structures with a multiplayer online game. Nature, 466:756-760, 2010.
10. M. de Condorcet. Essai sur l'application de l'analyse a la probabilite de decisions rendues a la pluralite de voix. Imprimerie Royal, 1785. Facsimile published in 1972 by Chelsea Publishing Company, New York.
11. C. Dwork, R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation methods for the web. In Proceedings of the 10th International World Wide Web Conference (WWW), pages 613-622, 2001.
12. C. Kenyon-Mathieu andW. Schudy. How to rank with few errors. In Proceedings of the 39th Annual ACM Symposium on Theory of Computing (STOC), pages 95-103, 2007.
13. T. Lu and C. Boutilier. Learning Mallows models with pairwise preferences. In Proceedings of the 28th International Conference on Machine Learning (ICML), pages 145-152, 2011.
14. C. L. Mallows. Non-null ranking models. Biometrika, 44:114-130, 1957.
15. L. Xia and V. Conitzer. Generalized scoring rules and the frequency of coalitional manipulability. In Proceedings of the 9th ACM Conference on Electronic Commerce (EC), pages 109-118, 2008.
16. L. Xia and V. Conitzer. A maximum likelihood approach towards aggregating partial orders. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pages 446-451, 2011.
17. L. Xia, V. Conitzer, and J. Lang. Aggregating preferences in multi-issue domains by using maximum likelihood estimators. In Proceedings of the 9th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pages 399-408, 2010.
18. H. P. Young. Condorcet's theory of voting. The American Political Science Review, 82(4):1231-1244, 1988.