4 citations · 5 across the 4 of their papers we have counts for
Showing stat.MLShow all
2 papers · 1 filter
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…
stat.ML2018
Efficient kernel-based variable selection with sparsistency
Xin He, Junhui Wang, Shaogao Lv
Variable selection is central to high-dimensional data analysis, and various algorithms have been developed. Ideally, a variable selection algorithm shall be flexible, scalable, an…