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Asymptotic normality and optimalities in estimation of large Gaussian graphical models
This paper considers a fundamental question: When is it possible to estimate low-dimensional parameters at parametric square-root rate in a large Gaussian graphical model?
Calibrated Elastic Regularization in Matrix Completion
This paper concerns the problem of matrix completion, which is to estimate a matrix from observations in a small subset of indices.
Sparse Matrix Inversion with Scaled Lasso
The penalty level of the scaled Lasso for each column is completely determined by data via convex minimization, without using cross-validation.
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