Uncertainty in Artificial Intelligence
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A Maximum Likelihood Approach For Selecting Sets of Alternatives
Ariel Procaccia, Sashank Reddi, Nisarg Shah
We consider the problem of selecting a subset of alternatives given noisy evaluations of the relative strength of different alternatives. We wish to select a k-subset (for a given k) that provides a maximum likelihood estimate for one of several objectives, e.g., containing the strongest alternative. Although this problem is NP-hard, we show that when the noise level is sufficiently high, intuitive methods provide the optimal solution. We thus generalize classical results about singling out one alternative and identifying the hidden ranking of alternatives by strength. Extensive experiments show that our methods perform well in practical settings.
Pages: 695-704
PS Link:
PDF Link: /papers/12/p695-procaccia.pdf
AUTHOR = "Ariel Procaccia and Sashank Reddi and Nisarg Shah",
TITLE = "A Maximum Likelihood Approach For Selecting Sets of Alternatives",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "695--704"

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