5 citations · 9 across the 8 of their papers we have counts for
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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…
Estimation of sparse linear regression coefficients under -subexponential covariates
Takeyuki Sasai
We tackle estimating sparse coefficients in a linear regression when the covariates are sampled from an -subexponential random vector. This vector belongs to a class of distribu…
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…
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…