6 papers
An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
Takeyuki Sasai, Hironori Fujisawa
Sparse linear regression methods such as Lasso require a tuning parameter that depends on the noise variance, which is typically unknown and difficult to estimate in practice. In t…
Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers
Takeyuki Sasai, Hironori Fujisawa
We investigate a problem estimating coefficients of linear regression under sparsity assumption when covariates and noises are sampled from heavy tailed distributions. Additionally…
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
Takeyuki Sasai, Hironori Fujisawa
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 sampl…
Adversarial robust weighted Huber regression
Takeyuki Sasai, Hironori Fujisawa
We consider a robust estimation of linear regression coefficients. In this note, we focus on the case where the covariates are sampled from an -subGaussian distribution with unk…
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
Takeyuki Sasai, Hironori Fujisawa
We consider robust low rank matrix estimation as a trace regression when outputs are contaminated by adversaries. The adversaries are allowed to add arbitrary values to arbitrary o…
Robust estimation with Lasso when outputs are adversarially contaminated
Takeyuki Sasai, Hironori Fujisawa
We consider robust estimation when outputs are adversarially contaminated. Nguyen and Tran (2012) proposed an extended Lasso for robust parameter estimation and then they showed th…