arXiv (All)
open access
L1 Regularization Paths in Linear Models by Parametric Gaussian Message Passing
The paper proposes new algorithms for computing the regularization path of linear-model fitting (i) with L1 costs for the independent variables and L2 costs for the dependent variables, and (ii) with L2 costs for the independent variables and L1 costs for the dependent variables. Unlike previously p
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
- Robustness of Regularized Regression Methods Under Compound Model Misspecification: A Simulation Benchmarking Study#276654
- Adaptive Regularization via Extreme Value Distributions for Gaussian Graphical Models#972729
- Robustifying Empirical Bayes#1344514
- Choosing the penalty in nonparametric regression: short and long-range dependence#1077160
- First-Order Methods for Wasserstein Distributionally Robust Constrained Optimization#1014766
Record · ID 1343562
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