Publications by authors named "Yongdao Zhou"
Data-driven decision-making has become crucial across various domains. Randomization and re-randomization are standard techniques employed in controlled experiments to estimate causal effects in the presence of numerous pre-treatment covariates. This paper quantifies the worst-case mean squared error of the difference-in-means estimator as a generalized discrepancy of covariates between treatment and control groups.
View Article and Find Full Text PDFRedundancy-Free Self-Supervised Relational Learning for Graph Clustering.
IEEE Trans Neural Netw Learn Syst
September 2023
Article Synopsis
- Graph clustering is a crucial data analysis task that uses graph neural networks but often fails to consider the relationships between nodes, leading to subpar clustering results.
- The authors introduce a new method called relational redundancy-free graph clustering (RFGC) that captures both attribute and structural relationships in graphs, aiming to improve node representation and clustering effectiveness.
- RFGC uses an autoencoder and a graph autoencoder to preserve important relationships while reducing redundant ones, and it also addresses oversmoothing issues, demonstrating better performance on benchmark datasets compared to existing methods.
Longitudinal data analysis has been very common in various fields. It is important in longitudinal studies to choose appropriate numbers of subjects and repeated measurements and allocation of time points as well. Therefore, existing studies proposed many criteria to select the optimal designs.
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