4 citations · 5 across the 4 of their papers we have counts for
5 papers
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…