12 citations · 18 across the 10 of their papers we have counts for
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stat.ML2021
SStaGCN: Simplified stacking based graph convolutional networks
Jia Cai, Zhilong Xiong, Shaogao Lv
Graph convolutional network (GCN) is a powerful model studied broadly in various graph structural data learning tasks. However, to mitigate the over-smoothing phenomenon, and deal…
math.ST2021★ 1 cited
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
Shaogao Lv, Xin He, Junhui Wang
This paper considers the partially functional linear model (PFLM) where all predictive features consist of a functional covariate and a high dimensional scalar vector. Over an infi…
stat.ML2021★ 4 cited
Generalization bounds for graph convolutional neural networks via Rademacher complexity
Shaogao Lv
This paper aims at studying the sample complexity of graph convolutional networks (GCNs), by providing tight upper bounds of Rademacher complexity for GCN models with a single hidd…