4 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
Transfer learning for tensor Gaussian graphical models
Mingyang Ren, Yaoming Zhen, Junhui Wang
Tensor Gaussian graphical models (GGMs), interpreting conditional independence structures within tensor data, have important applications in numerous areas. Yet, the available tens…
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…
Community Detection in General Hypergraph via Graph Embedding
Yaoming Zhen, Junhui Wang
Conventional network data has largely focused on pairwise interactions between two entities, yet multi-way interactions among multiple entities have been frequently observed in rea…
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…