Search Results for author:
Found 15 papers, 1 papers with code
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
This paper presents a comprehensive exploration of the theoretical properties inherent in the Adaptive Lasso and the Transfer Lasso.
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.
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.
Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
To address {these issues}, we propose a new estimation method for a linear DAG model with non-Gaussian noises.
Transfer Learning via $\ell_1$ Regularization
The proposed method has a tight estimation error bound under a stationary environment, and the estimate remains unchanged from the source estimate under small residuals.
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.
HMLasso: Lasso with High Missing Rate
Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with missing values, but it performs poorly when there are many missing values, so that the high missing rate problem has not been resolved.
Stochastic Gradient Descent for Stochastic Doubly-Nonconvex Composite Optimization
There is no convergence property when both composite functions are nonconvex, which is named the \textit{doubly-nonconvex} case. To overcome this difficulty, we assume a simple and weak condition that the penalty function is \textit{quasiconvex} and then we obtain convergence properties for the stochastic doubly-nonconvex composite optimization problem. The convergence rate obtained here is of the same order as the existing work. We deeply analyze the convergence rate with the constant step size and mini-batch size and give the optimal convergence rate with appropriate sizes, which is superior to the existing work.
Robust and Sparse Regression in GLM by Stochastic Optimization
Particularly, we show the linear regression, logistic regression and Poisson regression with $L_1$ regularization in detail as specific examples of robust and sparse GLM.
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
However, one of the biggest issues in sparse regularization is that its performance is quite sensitive to correlations between features.
Sparse principal component regression for generalized linear models
The basic loss function is based on a combination of the regression loss and PCA loss.
Robust and Sparse Regression via $γ$-divergence
The loss function is constructed by an empirical estimate of the $\gamma$-divergence with sparse regularization and the parameter estimate is defined as the minimizer of the loss function.
Sparse principal component regression with adaptive loading
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables.
Affine Invariant Divergences associated with Composite Scores and its Applications
By using the equivariant estimators under the affine transformation, one can obtain estimators that do no essentially depend on the choice of the system of units in the measurement.
