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In the problem of structured signal recovery from high-dimensional linear observations, it is commonly assumed that full-precision measurements are available.
On milman’s inequality and random subspaces which escape through a mesh in ℝ n \mathbb{R}^{n}
Yehoram Gordon · 1988
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Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors
Laurent Jacques, Jason N Laska, Petros T Boufounos, and Richard G Baraniuk · 2013
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Albert Ai, Alex Lapanowski, Yaniv Plan, and Roman Vershynin · 2014
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Joel A Tropp · 2015
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Samet Oymak and Mahdi Soltanolkotabi · 2016
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The generalized lasso with non-linear observations
Yaniv Plan and Roman Vershynin · 2016
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Exponential decay of reconstruction error from binary measurements of sparse signals
Richard G Baraniuk, Simon Foucart, Deanna Needell, Yaniv Plan, and Mary Wootters · 2017
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High-dimensional estimation of structured signals from non-linear observations with general convex loss functions
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Samet Oymak and Joel A Tropp · 2015
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Vidyashankar Sivakumar, Arindam Banerjee, and Pradeep K Ravikumar · 2015
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LASSO with non-linear measurements is equivalent to one with linear measurements
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2015
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Cvx: Matlab software for disciplined convex programming, version 2.1
Michael Grant, Stephen Boyd, Michael Grant, and Stephen Boyd
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High-Dimensional Probability: An Introduction with Applications in Data Science
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Robust one-bit compressed sensing with non-gaussian measurements
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