arXiv (All)
open access
A Stable Lasso
The Lasso has been widely used as a method for variable selection, valued for its simplicity and empirical performance. However, Lasso's selection stability deteriorates in the presence of correlated predictors. Several approaches have been developed to mitigate this limitation. In this paper, we pr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Stability Selection via Variable Decorrelation#1399712
- The Adaptive Lasso with Weighted IPW Factor and KRR Penalty Function#1232844
- Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation#821858
- Machine Learning Methods Through the Lens of Infinite-Dimensional Lasso#634850
- Autotune: fast, accurate, and automatic tuning parameter selection for Lasso#798695
Record · ID 1399751
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive