Data shared Lasso: a novel tool to discover uplift
From MaRDI portal
Publication:1659082
DOI10.1016/j.csda.2016.02.015zbMath1466.62082OpenAlexW2301046523WikidataQ42698240 ScholiaQ42698240MaRDI QIDQ1659082
Samuel M. Gross, Robert Tibshirani
Publication date: 15 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2016.02.015
high-dimensional regressionmulti-task learningsentiment analysisupliftclinical studies\(\ell_1\) penalization
Related Items
Unnamed Item, A joint estimation for the high-dimensional regression modeling on stratified data, Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data, Transfer Learning under High-dimensional Generalized Linear Models
Uses Software
Cites Work
- Unnamed Item
- Exact post-selection inference, with application to the Lasso
- A lasso for hierarchical interactions
- Convex multi-task feature learning
- The composite absolute penalties family for grouped and hierarchical variable selection
- A significance test for the lasso
- A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
- Model Selection and Estimation in Regression with Grouped Variables
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Strong Rules for Discarding Predictors in Lasso-Type Problems
- Causal Inference Using Potential Outcomes