no code implementations • 6 Jun 2020 • Zhibing Zhao, Ao Liu, Lirong Xia
We extend mixtures of RUMs with features to models that generate incomplete preferences and characterize their identifiability.
no code implementations • 17 May 2020 • Zhibing Zhao, Yingce Xia, Tao Qin, Lirong Xia, Tie-Yan Liu
Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc.
1 code implementation • NeurIPS 2019 • Zhibing Zhao, Lirong Xia
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).
no code implementations • ICLR 2019 • Zhibing Zhao, Yingce Xia, Tao Qin, Tie-Yan Liu
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.
no code implementations • 16 Jan 2019 • Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making.
no code implementations • ICML 2018 • 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.
no code implementations • 17 May 2018 • Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
We also propose novel ILP formulations for PUT-winners under STV and RP, respectively.
1 code implementation • 14 May 2018 • Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey Kephart, Nicholas Mattei, Hui Su, Lirong Xia
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
no code implementations • 23 Mar 2016 • 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.