9 papers
Conditional Local Independence Testing for Itô processes with Applications to Dynamic Causal Discovery
Mingzhou Liu, Xinwei Sun, Yizhou Wang
Inferring causal relationships from dynamical systems is the central interest of many scientific inquiries. Conditional local independence, which describes whether the evolution of…
Bayesian Intervention Optimization for Causal Discovery
Yuxuan Wang, Mingzhou Liu, Xinwei Sun +2
Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often…
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
Yong Wu, Mingzhou Liu, Jing Yan +4
Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the con…
Learning Causal Alignment for Reliable Disease Diagnosis
Mingzhou Liu, Ching-Wen Lee, Xinwei Sun +2
Aligning the decision-making process of machine learning algorithms with that of experienced radiologists is crucial for reliable diagnosis. While existing methods have attempted t…
Causal Discovery via Conditional Independence Testing with Proxy Variables
Mingzhou Liu, Xinwei Sun, Yu Qiao +1
Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias…
Causal Discovery from Subsampled Time Series with Proxy Variables
Mingzhou Liu, Xinwei Sun, Lingjing Hu +1
Inferring causal structures from time series data is the central interest of many scientific inquiries. A major barrier to such inference is the problem of subsampling, i.e., the f…
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition
Mingzhou Liu, Xinwei Sun, Fandong Zhang +2
Context, as referred to situational factors related to the object of interest, can help infer the object's states or properties in visual recognition. As such contextual features a…
Which Invariance Should We Transfer? A Causal Minimax Learning Approach
Mingzhou Liu, Xiangyu Zheng, Xinwei Sun +2
A major barrier to deploying current machine learning models lies in their non-reliability to dataset shifts. To resolve this problem, most existing studies attempted to transfer s…
A multimodal lossless coding method for skeletons in videos
Mingzhou Liu, Xiaoyi He, Weiyao Lin +4
Nowadays, skeleton information in videos plays an important role in human-centric video analysis but effective coding such massive skeleton information has never been addressed in…