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We study the problem of recovering a structured signal from independently and identically drawn linear measurements.
Some inequalities for gaussian processes and applications
Yehoram Gordon · 1985
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On milman’s inequality and random subspaces which escape through a mesh in R n
Yehoram Gordon · 1988
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J Candes, Justin K Romberg, and Terence Tao · 2006
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Compressed sensing
David L Donoho et al · 2006
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Sparse reconstruction by convex relaxation: Fourier and gaussian measurements
Mark Rudelson and Roman Vershynin · 2006
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The restricted isometry property and its implications for compressed sensing
Emmanuel J Candes · 2008
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Operator norm consistent estimation of large-dimensional sparse covariance matrices
Noureddine El Karoui et al · 2008
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Exact matrix completion via convex optimization
Emmanuel J Candès and Benjamin Recht · 2009
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Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
David Donoho and Jared Tanner · 2009
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Message-passing algorithms for compressed sensing
David L Donoho, Arian Maleki, and Andrea Montanari · 2009
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Various thresholds for l1-optimization in compressed sensing
Mihailo Stojnic · 2009
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Beyond nyquist: Efficient sampling of sparse bandlimited signals
Joel A Tropp, Jason N Laska, Marco F Duarte, Justin K Romberg, and Richard G Baraniuk · 2009
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Latent variable graphical model selection via convex optimization
Venkat Chandrasekaran, Pablo A Parrilo, and Alan S Willsky · 2010
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New null space results and recovery thresholds for matrix rank minimization
Samet Oymak and Babak Hassibi · 2010
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Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
Benjamin Recht, Maryam Fazel, and Pablo A Parrilo · 2010
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Estimation of simultaneously structured covariance matrices from quadratic measurements
Yuxin Chen, Yuejie Chi, and Andrea J Goldsmith · 2014
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Gespar: Efficient phase retrieval of sparse signals
Yoav Shechtman, Amir Beck, and Yonina C Eldar · 2014
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Rop: Matrix recovery via rank-one projections
T Tony Cai, Anru Zhang, et al · 2015
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Exact and stable covariance estimation from quadratic sampling via convex programming
Yuxin Chen, Yuejie Chi, and Andrea J Goldsmith · 2015
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Simultaneously structured models with application to sparse and low-rank matrices
Samet Oymak, Amin Jalali, Maryam Fazel, Yonina C Eldar, and Babak Hassibi · 2015
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Regularized linear regression: A precise analysis of the estimation error
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Compressive wideband power spectrum estimation
Dyonisius Dony Ariananda and Geert Leus · 2012
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The convex geometry of linear inverse problems
Venkat Chandrasekaran, Benjamin Recht, Pablo A Parrilo, and Alan S Willsky · 2012
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Phaselift: Exact and stable signal recovery from magnitude measurements via convex programming
Emmanuel J Candes, Thomas Strohmer, and Vladislav Voroninski · 2013
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Sparse signal recovery from quadratic measurements via convex programming
Xiaodong Li and Vladislav Voroninski · 2013
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Upper-bounding l1-optimization weak thresholds
Mihailo Stojnic · 2013
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Living on the edge: Phase transitions in convex programs with random data
Dennis Amelunxen, Martin Lotz, Michael B McCoy, and Joel A Tropp · 2014
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Christos Thrampoulidis, Samet Oymak, and Babak Hassibi · 2015
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The local convexity of solving systems of quadratic equations
Chris D White, Sujay Sanghavi, and Rachel Ward · 2015
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Low-rank positive semidefinite matrix recovery from corrupted rank-one measurements
Yuanxin Li, Yue Sun, and Yuejie Chi · 2016
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Universality laws for randomized dimension reduction, with applications
Samet Oymak and Joel A Tropp · 2017
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A universal analysis of large-scale regularized least squares solutions
Ashkan Panahi and Babak Hassibi · 2017
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Precise error analysis of regularized m m -estimators in high dimensions
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2018
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