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Variable selection for sparse linear regression is the problem of finding, given an m x p matrix B and a target vector y, a sparse vector x such that Bx approximately equals y.
The distribution of rademacher sums
S. J. Montgomery-Smith · 1990
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Efficient probabilistic checkable proofs and applications to approximation
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The risk inflation criterion for multiple regression
D. P. Foster and E. I. George · 1994
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On the hardness of approximating minimization problems
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R. Raz · 1995
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The hardness of approximate optima in lattices, codes, and systems of linear equations
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Adaptive greedy approximations
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Approximation Algorithms for NP-hard Problems
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The projection games conjecture and the np-hardness of ln n-approximating set-cover
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A note on the hardness of sparse approximation
A. Çivril · 2013
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Analytical approach to parallel repetition
I. Dinur and D. Steurer · 2014
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Lower bounds on the performance of polynomial-time algorithms for sparse linear regression
Y. Zhang, M. J. Wainwright, and M. I. Jordan · 2014
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