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- W2613306064 abstract "We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation." @default.
- W2613306064 created "2017-05-19" @default.
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- W2613306064 date "2018-01-23" @default.
- W2613306064 modified "2023-09-23" @default.
- W2613306064 title "Learning Importance of Preferences" @default.
- W2613306064 doi "https://doi.org/10.29007/v68w" @default.
- W2613306064 hasPublicationYear "2018" @default.
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