5 citations · 15 across the 5 of their papers we have counts for
5 papers
Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
Naiyu Yin, Tian Gao, Yue Yu +1
Capturing the underlying structural causal relations represented by Directed Acyclic Graphs (DAGs) has been a fundamental task in various AI disciplines. Causal DAG learning via th…
Knowledge-augmented Deep Learning and Its Applications: A Survey
Zijun Cui, Tian Gao, Kartik Talamadupula +1
Deep learning models, though having achieved great success in many different fields over the past years, are usually data hungry, fail to perform well on unseen samples, and lack o…
DAGs with No Curl: An Efficient DAG Structure Learning Approach
Yue Yu, Tian Gao, Naiyu Yin +1
Recently directed acyclic graph (DAG) structure learning is formulated as a constrained continuous optimization problem with continuous acyclicity constraints and was solved iterat…
Integer Programming for Causal Structure Learning in the Presence of Latent Variables
Rui Chen, Sanjeeb Dash, Tian Gao
The problem of finding an ancestral acyclic directed mixed graph (ADMG) that represents the causal relationships between a set of variables is an important area of research on caus…
Type-augmented Relation Prediction in Knowledge Graphs
Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula +2
Knowledge graphs (KGs) are of great importance to many real world applications, but they generally suffer from incomplete information in the form of missing relations between entit…