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Found 9 papers, 2 papers with code
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
We extend mixtures of RUMs with features to models that generate incomplete preferences and characterize their identifiability.
Dual Learning: Theoretical Study and an Algorithmic Extension
Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc.
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
We prove that when the dataset consists of combinations of ranked top-$l_1$ and $l_2$-way (or choice data over up to $l_2$ alternatives), mixture of $k$ Plackett-Luce models is not identifiable when $l_1+l_2\le 2k-1$ ($l_2$ is set to $1$ when there are no $l_2$-way orders).
Dual Learning: Theoretical Study and Algorithmic Extensions
Based on the theoretical discoveries, we extend dual learning by introducing more related mappings and propose highly symmetric frameworks, cycle dual learning and multipath dual learning, in both of which we can leverage the feedback signals from additional domains to improve the qualities of the mappings.
Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making.
Composite Marginal Likelihood Methods for Random Utility Models
We propose a novel and flexible rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning random utility models (RUMs), which include the Plackett-Luce model.
Practical Algorithms for STV and Ranked Pairs with Parallel Universes Tiebreaking
We also propose novel ILP formulations for PUT-winners under STV and RP, respectively.
A Cost-Effective Framework for Preference Elicitation and Aggregation
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
Learning Mixtures of Plackett-Luce Models
In this paper we address the identifiability and efficient learning problems of finite mixtures of Plackett-Luce models for rank data.
