402 citations · 556 across the 19 of their papers we have counts for
6 papers · 1 filter
Optimal Clustering with Noisy Queries via Multi-Armed Bandit
Jinghui Xia, Zengfeng Huang
Motivated by many applications, we study clustering with a faulty oracle. In this problem, there are items belonging to unknown clusters, and the algorithm is allowed to as…
Lipschitz Bandits with Batched Feedback
Yasong Feng, Zengfeng Huang, Tianyu Wang
In this paper, we study Lipschitz bandit problems with batched feedback, where the expected reward is Lipschitz and the reward observations are communicated to the player in batche…
Graph Neural Networks Inspired by Classical Iterative Algorithms
Yongyi Yang, Tang Liu, Yangkun Wang +6
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependenci…
Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang +2
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in vario…
SCE: Scalable Network Embedding from Sparsest Cut
Shengzhong Zhang, Zengfeng Huang, Haicang Zhou +1
Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the unde…
Higher-order Weighted Graph Convolutional Networks
Songtao Liu, Lingwei Chen, Hanze Dong +3
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neig…