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Optimal Variable Clustering for High-Dimensional Matrix Valued Data
To extract the information from the dependence structure for clustering, we propose a new latent variable model for the features arranged in matrix form, with some unknown membership matrices representing the clusters for the rows and columns.
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning
Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis.
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