ML Anthology

Learning Voting Trees

Ariel D. Procaccia, Aviv Zohar, Yoni Peleg, Jeffrey S. Rosenschein
AAAI 2007 pp. 110-115
/aaai/2007/procaccia2007aaai-learning/

Abstract

Binary voting trees provide a succinct representation for a large and prominent class of voting rules. In this paper, we investigate the PAC-learnability of this class of rules. We show that, while in general a learning algorithm would require an exponential number of samples, if the number of leaves is polynomial in the size of the set of alternatives then a polynomial training set suffices. We apply these results in an emerging theory: automated design of voting rules by learning.

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Cite

Text

Procaccia et al. "Learning Voting Trees." AAAI Conference on Artificial Intelligence, 2007.

Markdown

[Procaccia et al. "Learning Voting Trees." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/procaccia2007aaai-learning/)

BibTeX

@inproceedings{procaccia2007aaai-learning,
  title     = {{Learning Voting Trees}},
  author    = {Procaccia, Ariel D. and Zohar, Aviv and Peleg, Yoni and Rosenschein, Jeffrey S.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2007},
  pages     = {110-115},
  url       = {https://mlanthology.org/aaai/2007/procaccia2007aaai-learning/}
}