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» Robust Regression and Lasso
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NIPS
2008
11 years 2 months ago
Robust Regression and Lasso
We consider robust least-squares regression with feature-wise disturbance. We show that this formulation leads to tractable convex optimization problems, and we exhibit a particul...
Huan Xu, Constantine Caramanis, Shie Mannor
NN
2010
Springer
189views Neural Networks» more  NN 2010»
10 years 8 months ago
Sparse kernel learning with LASSO and Bayesian inference algorithm
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is co...
Junbin Gao, Paul W. Kwan, Daming Shi
ICASSP
2011
IEEE
10 years 5 months ago
Robust nonparametric regression by controlling sparsity
Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeat...
Gonzalo Mateos, Georgios B. Giannakis
CSDA
2007
114views more  CSDA 2007»
11 years 1 months ago
Relaxed Lasso
The Lasso is an attractive regularisation method for high dimensional regression. It combines variable selection with an eļ¬ƒcient computational procedure. However, the rate of co...
Nicolai Meinshausen
ICML
2010
IEEE
11 years 2 months ago
A scalable trust-region algorithm with application to mixed-norm regression
We present a new algorithm for minimizing a convex loss-function subject to regularization. Our framework applies to numerous problems in machine learning and statistics; notably,...
Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon
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