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Found 6 papers, 0 papers with code
Estimation of sparse linear regression coefficients under $L$-subexponential covariates
However, these methods require stronger conditions than those used for Gaussian random vectors to derive the error bounds.
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
We consider outlier-robust and sparse estimation of linear regression coefficients, when the covariates and the noises are contaminated by adversarial outliers and noises are sampled from a heavy-tailed distribution.
Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates
Robust and sparse estimation of linear regression coefficients is investigated.
Adversarial robust weighted Huber regression
We consider a robust estimation of linear regression coefficients.
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
We deal with matrix compressed sensing, including lasso as a partial problem, and matrix completion, and then we obtain sharp estimation error bounds.
Robust estimation with Lasso when outputs are adversarially contaminated
Nguyen and Tran (2012) proposed an extended Lasso for robust parameter estimation and then they showed the convergence rate of the estimation error.
