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This paper studies ordered weighted L1 (OWL) norm regularization for sparse estimation problems with strongly correlated variables.
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P. Bühlmann, P. Rüttiman, S. van de Geer, and C.-H. Zhang, “Correlated variables in regression: Clustering and sparse estimation,” Journal of Statistical Planning and Inference
2013
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M. Jaggi, “Revisiting Frank-Wolfe: projection-free sparse convex optimization”, Proceedings of the 30th International Conference on Machine Learning
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C. Genovese, J. Jin, L. Wasserman, and Z. Yao, “A Comparison of the LASSO and marginal regression”, Journal of Machine Learning Research
2012
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N. Rao, B. Recht, and R. Nowak, “Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals,” Proceedings of AISTATS
2012
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L. Zhong and J. Kwok, “Efficient sparse modeling with automatic feature grouping”, IEEE Transactions on Neural Networks and Learning Systems
2012
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2014
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2014
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X. Zeng, M. Figueiredo, “Decreasing Weighted Sorted ℓ 1 \ell_{1} Regularization”, IEEE Signal Processing Letters,
2014
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X. Zeng, M. Figueiredo, “The atomic norm formulation of OSCAR regularization with application to the Frank-Wolfe algorithm”, Proceedings of the European Signal Processing Conference
2014
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