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08/30/2023
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
This paper presents a comprehensive exploration of the theoretical prope...
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08/24/2022
Outlier Robust and Sparse Estimation of Linear Regression Coefficients
We consider outlier robust and sparse estimation of linear regression co...
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06/26/2021
Outlier-Resistant Estimators for Average Treatment Effect in Causal Inference
Estimators for causal quantities sometimes suffer from outliers. We inve...
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02/22/2021
Adversarial robust weighted Huber regression
We propose a novel method to estimate the coefficients of linear regress...
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10/25/2020
Adversarial Robust Low Rank Matrix Estimation: Compressed Sensing and Matrix Completion
We consider robust low rank matrix estimation when random noise is heavy...
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09/07/2020
Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis
We consider the problem of inferring the causal structure from observati...
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06/26/2020
Transfer Learning via ℓ_1 Regularization
Machine learning algorithms typically require abundant data under a stat...
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04/13/2020
Robust estimation with Lasso when outputs are adversarially contaminated
We consider robust estimation when outputs are adversarially contaminate...
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11/01/2018
HMLasso: Lasso for High Dimensional and Highly Missing Data
Sparse regression such as Lasso has achieved great success in dealing wi...
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05/21/2018
Stochastic Gradient Descent for Stochastic Doubly-Nonconvex Composite Optimization
The stochastic gradient descent has been widely used for solving composi...
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05/16/2018
On Difference Between Two Types of γ-divergence for Regression
The γ-divergence is well-known for having strong robustness against heav...
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02/15/2018
Robust and sparse Gaussian graphical modeling under cell-wise contamination
Graphical modeling explores dependences among a collection of variables ...
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02/09/2018
Robust and Sparse Regression in GLM by Stochastic Optimization
The generalized linear model (GLM) plays a key role in regression analys...
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11/06/2017
Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Sparse regularization such as ℓ_1 regularization is a quite powerful and...
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09/28/2016
Sparse principal component regression for generalized linear models
Principal component regression (PCR) is a widely used two-stage procedur...
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04/22/2016
Robust and Sparse Regression via γ-divergence
In high-dimensional data, many sparse regression methods have been propo...
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02/26/2014
Sparse principal component regression with adaptive loading
Principal component regression (PCR) is a two-stage procedure that selec...
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05/11/2013
