13 citations · 43 across the 8 of their papers we have counts for
8 papers
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences
Zhibing Zhao, Ao Liu, Lirong Xia
Random Utility Models (RUMs), which subsume Plackett-Luce model (PL) as a special case, are among the most popular models for preference learning. In this paper, we consider RUMs w…
Dual Learning: Theoretical Study and an Algorithmic Extension
Zhibing Zhao, Yingce Xia, Tao Qin +2
Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc. The high-level idea of dual lea…
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
Zhibing Zhao, Lirong Xia
Mixtures of ranking models have been widely used for heterogeneous preferences. However, learning a mixture model is highly nontrivial, especially when the dataset consists of part…
Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking
Jun Wang, Sujoy Sikdar, Tyler Shepherd +3
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making. They proceed in multiple rounds, and are affected by how ties are broken in each round. Howev…
Composite Marginal Likelihood Methods for Random Utility Models
Zhibing Zhao, Lirong Xia
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
Jun Wang, Sujoy Sikdar, Tyler Shepherd +3
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making. They proceed in multiple rounds, and are affected by how ties are broken in each round. Howev…
A Cost-Effective Framework for Preference Elicitation and Aggregation
Zhibing Zhao, Haoming Li, Junming Wang +4
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the…
Learning Mixtures of Plackett-Luce Models
Zhibing Zhao, Peter Piech, Lirong Xia
In this paper we address the identifiability and efficient learning problems of finite mixtures of Plackett-Luce models for rank data. We prove that for any , the mixture…