Date of Original Version
Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org).
Abstract or Description
We present the first model of optimal voting under adversarial noise. From this viewpoint, voting rules are seen as errorcorrecting codes: their goal is to correct errors in the input rankings and recover a ranking that is close to the ground truth. We derive worst-case bounds on the relation between the average accuracy of the input votes, and the accuracy of the output ranking. Empirical results from real data show that our approach produces significantly more accurate rankings than alternative approaches
Proceedings of the AAAI Conference on Artificial Intelligence, 2015, 1000-1006.