16 citations · 29 across the 16 of their papers we have counts for
6 papers · 1 filter
Flattened Graph Convolutional Networks For Recommendation
Yue Xu, Hao Chen, Zengde Deng +2
Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform rec…
Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization
Qixin Zhang, Zengde Deng, Xiangru Jian +3
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics. In this paper, we present two communication-efficient decentralized…
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback
Qixin Zhang, Zengde Deng, Zaiyi Chen +3
In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domai…
Online Primal-Dual Algorithms For Stochastic Resource Allocation Problems
Yuwei Chen, Zengde Deng, Zaiyi Chen +3
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints and conditional expectation constraints. The online RAP is an integer linear prog…
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
Hao Chen, Zhong Huang, Yue Xu +4
The recently proposed Graph Convolutional Networks (GCNs) have achieved significantly superior performance on various graph-related tasks, such as node classification and recommend…
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Qixin Zhang, Zengde Deng, Zaiyi Chen +2
In this paper, we revisit Stochastic Continuous Submodular Maximization in both offline and online settings, which can benefit wide applications in machine learning and operations…