Conference paper
Ranking-based evaluation of regression models
Saharon Rosset, Claudia Perlich, et al.
ICDM 2005
The Lasso achieves variance reduction and variable selection by solving an ℓ1-regularized least squares problem. Huang (2003) claims that 'there always exists an interval of regularization parameter values such that the corresponding mean squared prediction error for the Lasso estimator is smaller than for the ordinary least square estimator'. This result is correct. However, its proof in Huang (2003) is not. This paper presents a corrected proof of the claim, which exposes and uses some interesting fundamental properties of the Lasso.
Saharon Rosset, Claudia Perlich, et al.
ICDM 2005
Sivan Sabato, Elad Yom-Tov, et al.
NSDI 2007
Saharon Rosset
Bioinformatics
Saharon Rosset, Ehud Aharoni, et al.
Genetic Epidemiology