Algorithms in combinatorial geometry
Algorithms in combinatorial geometry
Machine learning: a theoretical approach
Machine learning: a theoretical approach
An automated meeting scheduling system that utilizes user preferences
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Voting for movies: the anatomy of a recommender system
Proceedings of the third annual conference on Autonomous Agents
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A heuristic technique for multi-agent planning
Annals of Mathematics and Artificial Intelligence
Applying learning algorithms to preference elicitation
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
An Algorithm for Automatically Designing Deterministic Mechanisms without Payments
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Learning from revealed preference
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Journal of Computer and System Sciences
When are elections with few candidates hard to manipulate?
Journal of the ACM (JACM)
Anyone but him: The complexity of precluding an alternative
Artificial Intelligence
Algorithms for the coalitional manipulation problem
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Generalized scoring rules and the frequency of coalitional manipulability
Proceedings of the 9th ACM conference on Electronic commerce
Junta distributions and the average-case complexity of manipulating elections
Journal of Artificial Intelligence Research
Winner determination in sequential majority voting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-winner elections: complexity of manipulation, control, and winner-determination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Universal voting protocol tweaks to make manipulation hard
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
How hard is it to control an election?
Mathematical and Computer Modelling: An International Journal
Incentive compatible regression learning
Journal of Computer and System Sciences
Winner determination in voting trees with incomplete preferences and weighted votes
Autonomous Agents and Multi-Agent Systems
Algorithms for strategyproof classification
Artificial Intelligence
On the approximability of Dodgson and Young elections
Artificial Intelligence
Optimal social choice functions: a utilitarian view
Proceedings of the 13th ACM Conference on Electronic Commerce
Multiagent systems, and the search for appropriate foundations
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Designing social choice mechanisms using machine learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Generalized scoring rules: a framework that reconciles Borda and Condorcet
ACM SIGecom Exchanges
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Scoring rules and voting trees are two broad and concisely-representable classes of voting rules; scoring rules award points to alternatives according to their position in the preferences of the voters, while voting trees are iterative procedures that select an alternative based on pairwise comparisons. In this paper, we investigate the PAC-learnability of these classes of rules. We demonstrate that the class of scoring rules, as functions from preferences into alternatives, is efficiently learnable in the PAC model. With respect to voting trees, while in general a learning algorithm would require an exponential number of samples, we show that 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.