Sparse Nonlinear Regression: Parameter Estimation Under Nonconvexity
Abstract
We study parameter estimation for sparse nonlinear regression. More specifically, we assume the data are given by y = f( \bf x^T \bf β^* ) + ε, where f is nonlinear. To recover \bf βs, we propose an \ell_1-regularized least-squares estimator. Unlike classical linear regression, the corresponding optimization problem is nonconvex because of the nonlinearity of f. In spite of the nonconvexity, we prove that under mild conditions, every stationary point of the objective enjoys an optimal statistical rate of convergence. Detailed numerical results are provided to back up our theory.
Cite
Text
Yang et al. "Sparse Nonlinear Regression: Parameter Estimation Under Nonconvexity." International Conference on Machine Learning, 2016.Markdown
[Yang et al. "Sparse Nonlinear Regression: Parameter Estimation Under Nonconvexity." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/yang2016icml-sparse/)BibTeX
@inproceedings{yang2016icml-sparse,
title = {{Sparse Nonlinear Regression: Parameter Estimation Under Nonconvexity}},
author = {Yang, Zhuoran and Wang, Zhaoran and Liu, Han and Eldar, Yonina and Zhang, Tong},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {2472-2481},
volume = {48},
url = {https://mlanthology.org/icml/2016/yang2016icml-sparse/}
}