26 citations · 178 across the 17 of their papers we have counts for
19 papers
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
Feng Xie, Biwei Huang, Zhengming Chen +4
We investigate the task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal…
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Biwei Huang, Charles Jia Han Low, Feng Xie +2
Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems. In this paper, we consider a challenging…
Action-Sufficient State Representation Learning for Control with Structural Constraints
Biwei Huang, Chaochao Lu, Liu Leqi +4
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information requ…
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
Wei Chen, Kun Zhang, Ruichu Cai +4
We consider the problem of estimating a particular type of linear non-Gaussian model. Without resorting to the overcomplete Independent Component Analysis (ICA), we show that under…
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
Feng Xie, Ruichu Cai, Biwei Huang +3
Causal discovery aims to recover causal structures or models underlying the observed data. Despite its success in certain domains, most existing methods focus on causal relations b…
Domain Adaptation as a Problem of Inference on Graphical Models
Kun Zhang, Mingming Gong, Petar Stojanov +3
This paper is concerned with data-driven unsupervised domain adaptation, where it is unknown in advance how the joint distribution changes across domains, i.e., what factors or mod…