15 citations · 26 across the 3 of their papers we have counts for
3 papers
stat.ML2014★ 9 cited
Screening Rules for Overlapping Group Lasso
Seunghak Lee, Eric P. Xing
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero c…
stat.ML2014★ 15 cited
Primitives for Dynamic Big Model Parallelism
Seunghak Lee, Jin Kyu Kim, Xun Zheng +3
When training large machine learning models with many variables or parameters, a single machine is often inadequate since the model may be too large to fit in memory, while trainin…
stat.ML2012★ 2 cited
Structured Input-Output Lasso, with Application to eQTL Mapping, and a Thresholding Algorithm for Fast Estimation
Seunghak Lee, Eric P. Xing
We consider the problem of learning a high-dimensional multi-task regression model, under sparsity constraints induced by presence of grouping structures on the input covariates an…