12 citations · 18 across the 10 of their papers we have counts for
10 papers
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Shiyu Liu, Shaogao Lv, Dun Zeng +3
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their pri…
Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix
Shaogao Lv, Gang Wen, Shiyu Liu +2
To improve the robustness of graph neural networks (GNN), graph structure learning (GSL) has attracted great interest due to the pervasiveness of noise in graph data. Many approach…
Stability and Generalization of lp-Regularized Stochastic Learning for GCN
Shiyu Liu, Linsen Wei, Shaogao Lv +1
Graph convolutional networks (GCN) are viewed as one of the most popular representations among the variants of graph neural networks over graph data and have shown powerful perform…
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…
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…
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…
Learning rates for partially linear support vector machine in high dimensions
Yifan Xia, Yongchao Hou, Shaogao Lv
This paper analyzes a new regularized learning scheme for high dimensional partially linear support vector machine. The proposed approach consists of an empirical risk and the Lass…
Financial Market Directional Forecasting With Stacked Denoising Autoencoder
Shaogao Lv, Yongchao Hou, Hongwei Zhou
Forecasting stock market direction is always an amazing but challenging problem in finance. Although many popular shallow computational methods (such as Backpropagation Network and…
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…
Debiased distributed learning for sparse partial linear models in high dimensions
Shaogao Lv, Heng Lian
Although various distributed machine learning schemes have been proposed recently for pure linear models and fully nonparametric models, little attention has been paid on distribut…