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Low-rank matrix regression refers to the instances of recovering a low-rank matrix based on specially designed measurements and the corresponding noisy outcomes.
Data-dependent confidence regions of singular subspaces
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M. V. Klibanov, P. E. Sacks, and A. V. Tikhonravov · 1995
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Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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The geometry of algorithms with orthogonality constraints
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Regularization and variable selection via the elastic net
H. Zou and T. Hastie · 2005
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Model selection and estimation in regression with grouped variables
M. Yuan and Y. Lin · 2006
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Enhancing sparsity by reweighted ? 1 minimization
E. J. Candes, M. B. Wakin, and S. P. Boyd · 2008
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Matrix completion with noise
E. J. Candes and Y. Plan · 2010
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Introduction to the non-asymptotic analysis of random matrices
R. Vershynin · 2010
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Distributed optimization and statistical learning via the alternating direction method of multipliers
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein · 2011
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Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements
E. J. Candes and Y. Plan · 2011
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Recovering low-rank matrices from few coefficients in any basis
D. Gross · 2011
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Rank penalized estimators for high-dimensional matrices
O. Klopp · 2011
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Von neumann entropy penalization and low-rank matrix estimation
V. Koltchinskii · 2011
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Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
V. Koltchinskii, K. Lounici, and A. B. Tsybakov · 2011
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Estimation of (near) low-rank matrices with noise and high-dimensional scaling
S. Negahban and M. J. Wainwright · 2011
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Estimation of high-dimensional low-rank matrices
A. Rohde and A. B. Tsybakov · 2011
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User-friendly tail bounds for sums of random matrices
J. A. Tropp · 2012
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Phaselift: Exact and stable signal recovery from magnitude measurements via convex programming
E. J. Candes, T. Strohmer, and V. Voroninski · 2013
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Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance
V. Koltchinskii and K. Lounici · 2016
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Perturbation of linear forms of singular vectors under gaussian noise
V. Koltchinskii and D. Xia · 2016
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Estimation of low rank density matrices: bounds in schatten norms and other distances
D. Xia and V. Koltchinskii · 2016
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Confidence intervals for high-dimensional linear regression: Minimax rates and adaptivity
T. T. Cai and Z. Guo · 2017
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High dimensional deformed rectangular matrices with applications in matrix denoising
X. Ding · 2017
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Blind deconvolution using convex programming
A. Ahmed, B. Recht, and J. Romberg · 2014
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Solving quadratic equations via phaselift when there are about as many equations as unknowns
E. J. Candès and X. Li · 2014
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Confidence intervals and hypothesis testing for high-dimensional regression
A. Javanmard and A. Montanari · 2014
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A significance test for the lasso
R. Lockhart, J. Taylor, R. J. Tibshirani, and R. Tibshirani · 2014
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Confidence intervals for low dimensional parameters in high dimensional linear models
C.-H. Zhang and S. S. Zhang · 2014
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Uncertainty quantification for matrix compressed sensing and quantum tomography problems
A. Carpentier, J. Eisert, D. Gross, and R. Nickl · 2015
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V. Koltchinskii · 2017
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New asymptotic results in principal component analysis
V. Koltchinskii and K. Lounici · 2017
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Normal approximation and concentration of spectral projectors of sample covariance
V. Koltchinskii and K. Lounici · 2017
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C. Ma, K. Wang, Y. Chi, and Y. Chen · 2017
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Bayesian inference for spectral projectors of covariance matrix
I. Silin and V. Spokoiny · 2017
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Estimation of low rank density matrices by pauli measurements
D. Xia · 2017
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An iterative hard thresholding estimator for low rank matrix recovery with explicit limiting distribution
A. Carpentier and A. K. Kim · 2018
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Adaptive confidence sets for matrix completion
A. Carpentier, O. Klopp, M. Löffler, and R. Nickl · 2018
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Double/debiased machine learning for treatment and structural parameters
V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, and J. Robins · 2018
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On polynomial time methods for exact low rank tensor completion
D. Xia and M. Yuan · 2019
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