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High-dimensional inference refers to problems of statistical estimation in which the ambient dimension of the data may be comparable to or possibly even larger than the sample size.
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Concentration Inequalties and Model Selection
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Convex optimization
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Maximum-margin matrix factorization
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Maximum-margin matrix factorization
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Regularized estimation of large covariance matrices
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Exact matrix completion via convex optimization
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Compressed sensing and best k-term approximation
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Augmented sparse principal component analysis for high-dimensional data
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High-dimensional covariance estimation: Convergence rates of ℓ 1 \ell_{1} -regularized log-determinant divergence
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High-dimensional graphs and variable selection with the Lasso
N. Meinshausen and P. Bühlmann · 2006
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Model selection and estimation in regression with grouped variables
M. Yuan and Y. Lin · 2006
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Operator norm consistent estimation of large dimensional sparse covariance matrices
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Sparse inverse covariance estimation with the graphical Lasso
J. Friedman, T. Hastie, and R. Tibshirani · 2007
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Gradient methods for minimizing composite objective function
Y. Nesterov · 2007
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P. Ravikumar, M. J. Wainwright, G. Raskutti, and B. Yu · 2008
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High-dimensional analysis of semdefinite relaxations for sparse principal component analysis
A. A. Amini and M. J. Wainwright · 2009
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Simultaneous analysis of lasso and dantzig selector
P. Bickel, Y. Ritov, and A. Tsybakov · 2009
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J. Huang and T. Zhang · 2009
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An accelerated gradient method for trace norm minimization
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Matrix completion from noisy entires
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Taking advantage of sparsity in multi-task learning
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A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
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Minimax rates of estimation for high-dimensional linear regression over ℓ q \ell_{q} -balls
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Sparse permutation invariant covariance estimation
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Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (Lasso)
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