1 citations · 3 across the 7 of their papers we have counts for
7 papers
Low-rank matrix recovery via nonconvex optimization methods with application to errors-in-variables matrix regression
Xin Li, Dongya Wu
We consider the nonconvex regularized method for low-rank matrix recovery. Under the assumption on the singular values of the parameter matrix, we provide the recovery bound for an…
Adversarially robust generalization theory via Jacobian regularization for deep neural networks
Dongya Wu, Xin Li
Powerful deep neural networks are vulnerable to adversarial attacks. To obtain adversarially robust models, researchers have separately developed adversarial training and Jacobian…
Sparse deep neural networks for nonparametric estimation in high-dimensional sparse regression
Dongya Wu, Xin Li
Generalization theory has been established for sparse deep neural networks under high-dimensional regime. Beyond generalization, parameter estimation is also important since it is…
Low-rank matrix estimation via nonconvex spectral regularized methods in errors-in-variables matrix regression
Xin Li, Dongya Wu
High-dimensional matrix regression has been studied in various aspects, such as statistical properties, computational efficiency and application to specific instances including mul…
Low-rank matrix estimation in multi-response regression with measurement errors: Statistical and computational guarantees
Xin Li, Dongya Wu
In this paper, we investigate the matrix estimation problem in the multi-response regression model with measurement errors. A nonconvex error-corrected estimator based on a combina…
Minimax rates of -losses for high-dimensional linear regression models with additive measurement errors over -balls
Xin Li, Dongya Wu
We study minimax rates for high-dimensional linear regression with additive errors under the -losses, where the regression parameter is of weak sparsity.…
Sparse recovery via nonconvex regularized -estimators over -balls
Xin Li, Dongya Wu, Chong Li +2
In this paper, we analyse the recovery properties of nonconvex regularized -estimators, under the assumption that the true parameter is of soft sparsity. In the statistical aspe…