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We present a sparse analogue to stochastic gradient descent that is guaranteed to perform well under similar conditions to the lasso.
A stochastic approximation method
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Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, and Yoram Singer · 2006
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The Dantzig selector: Statistical estimation when p is much larger than n
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Sara A Van de Geer · 2008
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Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization
H Brendan McMahan · 2011
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Minimax rates of estimation for high-dimensional linear regression over l q l_{q} -balls
Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2011
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Shai Shalev-Shwartz · 2011
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Stochastic methods for L1-regularized loss minimization
Shai Shalev-Shwartz and Ambuj Tewari · 2011
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Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions
Alekh Agarwal, Sahand Negahban, and Martin J Wainwright · 2012
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Léon Bottou · 2012
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Strong rules for discarding predictors in lasso-type problems
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