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Computational Statistics & Data Analysis
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RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
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Proceedings of the 6th ACM conference on Electronic commerce
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Artificial Intelligence
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
Determining possible and necessary winners under common voting rules given partial orders
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Eliciting single-peaked preferences using comparison queries
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Proceedings of the 11th ACM conference on Electronic commerce
Practical voting rules with partial information
Autonomous Agents and Multi-Agent Systems
Vote elicitation with probabilistic preference models: empirical estimation and cost tradeoffs
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
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Mathematical and Computer Modelling: An International Journal
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Top-k voting is an especially natural form of partial vote elicitation in which only length k prefixes of rankings are elicited. We analyze the ability of top-k vote elicitation to correctly determine true winners, with high probability, given probabilistic models of voter preferences and candidate availability. We provide bounds on the minimal value of k required to determine the correct winner under the plurality and Borda voting rules, considering both worst-case preference profiles and profiles drawn from the impartial culture and Mallows probabilistic models. We also derive conditions under which the special case of zero-elicitation (i.e., k = 0) produces the correct winner. We provide empirical results that confirm the value of top-k voting.