4 citations · 6 across the 3 of their papers we have counts for
4 papers
Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers
Wei Zhou, Xin He, Wei Zhong +1
Directed acyclic graph (DAG) models are widely used to represent causal relationships among random variables in many application domains. This paper studies a special class of non-…
Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
Ruixuan Zhao, Xin He, Junhui Wang
Acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propo…
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…
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…