Arrow Research search

Author name cluster

Jason Ge

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

JMLR Journal 2019 Journal Article

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

  • Jason Ge
  • Xingguo Li
  • Haoming Jiang
  • Han Liu
  • Tong Zhang
  • Mengdi Wang
  • Tuo Zhao

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies. Besides, the library allows users to choose different sparsity-inducing regularizers, including the convex $\ell_1$, nonvoncex MCP and SCAD regularizers. The library is coded in \texttt{C++} and has user-friendly R and Python wrappers. Numerical experiments demonstrate that picasso can scale up to large problems efficiently. [abs] [ pdf ][ bib ] [ code ] [ webpage ] &copy JMLR 2019. ( edit, beta )

PDF Details

NeurIPS Conference 2017 Conference Paper

On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning

  • Xingguo Li
  • Lin Yang
  • Jason Ge
  • Jarvis Haupt
  • Tong Zhang
  • Tuo Zhao

We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions. Our proposed algorithm integrates the proximal newton algorithm with multi-stage convex relaxation based on the difference of convex (DC) programming, and enjoys both strong computational and statistical guarantees. Specifically, by leveraging a sophisticated characterization of sparse modeling structures (i. e. , local restricted strong convexity and Hessian smoothness), we prove that within each stage of convex relaxation, our proposed algorithm achieves (local) quadratic convergence, and eventually obtains a sparse approximate local optimum with optimal statistical properties after only a few convex relaxations. Numerical experiments are provided to support our theory.

PDF Details