6 citations · 12 across the 6 of their papers we have counts for
6 papers
A Knowledge-Informed Pretrained Model for Causal Discovery
Wenbo Xu, Yue He, Yunhai Wang +4
Causal discovery has been widely studied, yet many existing methods rely on strong assumptions or fall into two extremes: either depending on costly interventional signals or parti…
Detecting Unobserved Confounders: A Kernelized Regression Approach
Yikai Chen, Yunxin Mao, Chunyuan Zheng +7
Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous…
Decorrelated Clustering with Data Selection Bias
Xiao Wang, Shaohua Fan, Kun Kuang +3
Most of existing clustering algorithms are proposed without considering the selection bias in data. In many real applications, however, one cannot guarantee the data is unbiased. S…
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training dat…
Stable Learning via Sample Reweighting
Zheyan Shen, Peng Cui, Tong Zhang +1
We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of paramet…
Stable Prediction across Unknown Environments
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier wi…