Journal of the ACM (JACM)
Communication complexity of common voting rules
Proceedings of the 6th ACM conference on Electronic commerce
Supervised Ordering — An Empirical Survey
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The Generalized Maximum Coverage Problem
Information Processing Letters
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
Complexity of strategic behavior in multi-winner elections
Journal of Artificial Intelligence Research
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Assessing regret-based preference elicitation with the UTPREF recommendation system
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
TreeMatrix: A Hybrid Visualization of Compound Graphs
Computer Graphics Forum
Robust approximation and incremental elicitation in voting protocols
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Achieving fully proportional representation is easy in practice
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Multi-winner social choice considers the problem of selecting a slate of K options to realize some social objective. It has found application in the construction of political legislatures and committees, product recommendation, and related problems, and has recently attracted attention from a computational perspective. We address the multi-winner problem when facing incomplete voter preferences, using the notion of minimax regret to determine a robust slate of options in the presence of preference uncertainty. We analyze the complexity of this problem and develop new exact and greedy robust optimization algorithms for its solution. Using these techniques, we also develop preference elicitation heuristics which, in practice, allow us to find near-optimal slates with considerable savings in the preference information required vis-à-vis complete votes.