Publication date
April 2016
DOI Publisher
Wiley ISSN
0306-7734 Journal
International Statistical Review Citation count (estimate)
1Abstract
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/119115/1/insr12167.pd
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/119115/1/insr12167.pd
Assessment of model performance on sparse datasets with different degrees of sparsity (1–10 of 11 fe...
The public defense on 14th May 2020 at 16:00 (4 p.m.) will be organized via remote technology. Li...
The batch least-absolute shrinkage and selection operator (Lasso) has well-documented merits for est...
Taking the Lasso method as its starting point, this book describes the main ingredients needed to st...
Presented on September 6, 2018 from 3:05 p.m.-3:55 p.m. at the School of Mathematics, Skiles Room 00...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Presented on August 31, 2018 from 2:00 p.m.-3:00 p.m. at the Georgia Institute of Technology (Georgi...
Presented on September 4, 2018 from 11:00 a.m.-11:50 a.m. at the School of Mathematics, Skiles Room ...
<p>The set of causal SNPs influencing the phenotype are represented by boxes that are shaded grey. ...
Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in scie...
We give a short account of some recent results on sparse recovering in the context of learning theor
We present a data dependent generalization bound for a large class of regularized algorithms which i...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–10 of 11 fe...
The public defense on 14th May 2020 at 16:00 (4 p.m.) will be organized via remote technology. Li...
The batch least-absolute shrinkage and selection operator (Lasso) has well-documented merits for est...
Taking the Lasso method as its starting point, this book describes the main ingredients needed to st...
Presented on September 6, 2018 from 3:05 p.m.-3:55 p.m. at the School of Mathematics, Skiles Room 00...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Presented on August 31, 2018 from 2:00 p.m.-3:00 p.m. at the Georgia Institute of Technology (Georgi...
Presented on September 4, 2018 from 11:00 a.m.-11:50 a.m. at the School of Mathematics, Skiles Room ...
<p>The set of causal SNPs influencing the phenotype are represented by boxes that are shaded grey. ...
Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern...
The articles in this special section focus on learning adaptive models. Over the past few years, spa...
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in scie...
We give a short account of some recent results on sparse recovering in the context of learning theor
We present a data dependent generalization bound for a large class of regularized algorithms which i...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Assessment of model performance on sparse datasets with different degrees of sparsity (1–10 of 11 fe...
The public defense on 14th May 2020 at 16:00 (4 p.m.) will be organized via remote technology. Li...
The batch least-absolute shrinkage and selection operator (Lasso) has well-documented merits for est...
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