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Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
The Tien Mai
https://arxiv.org/abs/2506.07790 https:…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies on the squared loss, performs well under Gaussian noise assumptions but often deteriorates in the presence of heavy-tailed errors or outliers commonly encountered in real data applications such as genomics, finance, and signal processing. To address these challenges, we propose a novel robust regression method, terme…

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation
Shunxin Yao
https://arxiv.org/abs/2506.02790 https://

Orthogonality-Constrained Deep Instrumental Variable Model for Causal Effect Estimation
OC-DeepIV is a neural network model designed for estimating causal effects. It characterizes heterogeneity by adding interaction features and reduces redundancy through orthogonal constraints. The model includes two feature extractors, one for the instrumental variable Z and the other for the covariate X*. The training process is divided into two stages: the first stage uses the mean squared error (MSE) loss function, and the second stage incorporates orthogonal regularization. Experimental res…