Penalized Generative Variable Selection
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Abstract (arXiv)
Deep networks are increasingly applied to a wide variety of data, including data with high-dimensional predictors. In such analysis, variable selection can be needed along with estimation/model building. Many of the existing deep network studies that incorporate variable selection have been limited to methodological and numerical developments. In this study, we consider modeling/estimation using the conditional Wasserstein Generative Adversarial networks. Group Lasso penalization is applied for variable selection, which may improve model estimation/prediction, interpretability, stability, etc. Significantly advancing from the existing literature, the analysis of censored survival data is also considered. We establish the convergence rate for variable selection while considering the approximation error, and obtain a more efficient distribution estimation. Simulations and the analysis of real experimental data demonstrate satisfactory practical utility of the proposed analysis.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 42 references that are in the map. First posted on arXiv 26 Feb 2024.
Builds on
8 of 42 references.
References · 8
- 2017Improved Training of Wasserstein GANscited by 2,235
- 2019LassoNet: A Neural Network with Feature Sparsitycited by 82
- 2017Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classificationcited by 39
- 2020Consistent Feature Selection for Analytic Deep Neural Networkscited by 27
- 2021Wasserstein Generative Learning of Conditional Distributioncited by 24
- 2020A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributionscited by 7
- 2023Sparse-Input Neural Network using Group Concave Regularizationcited by 4
- 2022Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networkscited by 3
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Citing papers per year: 2026: 1.
1 paper.