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Sparse coding is a core building block in many data analysis and machine learning pipelines.
How to regularize a difference of convex functions
J. B. Hiriart-Urruty · 1991
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Robert Tibshirani · 1996
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Smooth minimization of non-smooth functions
Yu Nesterov · 2005
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Pathwise coordinate optimization
Jerome Friedman, Trevor Hastie, Holger Höfling, and Robert Tibshirani · 2007
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Least angle and ℓ1 penalized regression: A review
Tim Hesterberg, Nam Hee Choi, Lukas Meier, and Chris Fraley · 2008
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Amir Beck and Marc Teboulle · 2009
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Online Learning for Matrix Factorization and Sparse Coding
Julien Mairal, Francis Bach, Jean Ponce, and Guillermo Sapiro · 2009
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Coordinate descent optimization for l1 minimization with application to compressed sensing; a greedy algorithm
Stanley Osher and Yingying Li · 2009
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Learning Fast Approximations of Sparse Coding
Karol Gregor and Yann Le Cun · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
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Learning intermediate-level representations of form and motion from natural movies
Charles F Cadieu and Bruno A Olshausen · 2012
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Theory of convex optimization for machine learning
Sébastien Bubeck · 2014
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Fast randomized kernel ridge regression with statistical guarantees
Ahmed Alaoui and Michael W Mahoney · 2015
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Statistical Learning with Sparsity
Trevor Hastie, Robert Tibshirani, and Martin J. Wainwright · 2015
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Sharp time–data tradeoffs for linear inverse problems
Samet Oymak, Benjamin Recht, and Mahdi Soltanolkotabi · 2015
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Randomized sketches for kernels: Fast and optimal non-parametric regression
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Tradeoffs between convergence speed and reconstruction accuracy in inverse problems
Raja Giryes, Yonina C Eldar, Alex M Bronstein, and Guillermo Sapiro · 2016
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Learning Efficient Structured Sparse Models
Pablo Sprechmann, Alex Bronstein, and Guillermo Sapiro · 2012
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Computational and statistical tradeoffs via convex relaxation
Venkat Chandrasekaran and Michael I Jordan · 2013
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Maximal sparsity with deep networks?
Bo Xin, Yizhou Wang, Wen Gao, and David Wipf · 2016
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