447 citations · 782 across the 31 of their papers we have counts for
9 papers · 1 filter
Low-rank Matrix Recovery With Unknown Correspondence
Zhiwei Tang, Tsung-Hui Chang, Xiaojing Ye +1
We study a matrix recovery problem with unknown correspondence: given the observation matrix , where is an unknown permutation matrix, we aim to reco…
Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximation
Nathan Gaby, Fumin Zhang, Xiaojing Ye
We develop a versatile deep neural network architecture, called Lyapunov-Net, to approximate Lyapunov functions of dynamical systems in high dimensions. Lyapunov-Net guarantees pos…
Influence Estimation and Maximization via Neural Mean-Field Dynamics
Shushan He, Hongyuan Zha, Xiaojing Ye
We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems on heterogeneous diffusion networks. Our new framework leverages…
A Hypergradient Approach to Robust Regression without Correspondence
Yujia Xie, Yixiu Mao, Simiao Zuo +4
We consider a variant of regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world pro…
Network Diffusions via Neural Mean-Field Dynamics
Shushan He, Hongyuan Zha, Xiaojing Ye
We propose a novel learning framework based on neural mean-field dynamics for inference and estimation problems of diffusion on networks. Our new framework is derived from the Mori…
Learning Cost Functions for Optimal Transport
Shaojun Ma, Haodong Sun, Xiaojing Ye +2
Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples. In this paper, we derive an unconstrained con…
Learning Deep Mean Field Games for Modeling Large Population Behavior
Jiachen Yang, Xiaojing Ye, Rakshit Trivedi +2
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete t…
Wasserstein Learning of Deep Generative Point Process Models
Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye +3
Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world pheno…
Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye +6
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. Th…