Lasso Estimator

The Lasso estimator is a powerful regularization technique used in high-dimensional regression to select relevant variables and improve prediction accuracy by adding a penalty to the size of the regression coefficients. Current research focuses on extending its application beyond linear models, including its use in nonparametric regression (e.g., trend filtering), graphical models (for structure learning), and neural networks (for variable selection and system identification). This work addresses challenges such as robustness to outliers, handling correlated data, and efficient computation in distributed settings, ultimately improving the reliability and applicability of Lasso-based methods across diverse scientific and engineering domains.

Papers

October 3, 2024

Minmax Trend Filtering: A Locally Adaptive Nonparametric Regression Method via Pointwise Min Max Optimization
Sabyasachi Chatterjee
Min Max Standard LASSO Local Regression Lasso Estimator Trend Filtering

December 28, 2023

Inconsistency of cross-validation for structure learning in Gaussian graphical models
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
Cross Validation Graphical Model Structure Learning Hard to Easy Inconsistency Gaussian Graphical Model Lasso Estimator

May 17, 2023

Exact Recovery for System Identification with More Corrupt Data than Clean Data
Baturalp Yalcin, Haixiang Zhang, Javad Lavaei, Murat Arcak
Adversarial Attack Discrete Time System Identification Clean Data Attack Vector Exact Community Recovery Lasso Estimator

May 7, 2023

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization
Gen Li, Ganghua Wang, Jie Ding
Two Layer ReLU Standard LASSO Partial Identifiability Lasso Estimator

April 18, 2023

The Adaptive $\tau$-Lasso: Robustness and Oracle Properties
Emadaldin Mozafari-Majd, Visa Koivunen
Native Robustness Adaptive Importance Test Oracle Sparse Optimization Lasso Estimator Norm Penalty

February 22, 2023

Quantized Low-Rank Multivariate Regression with Random Dithering
Junren Chen, Yueqi Wang, Michael K. Ng
Low Rank Constrained Learning Multivariate Regression Uniform Quantization Lasso Estimator

January 30, 2023

Structure Learning and Parameter Estimation for Graphical Models via Penalized Maximum Likelihood Methods
Maryia Shpak
Bayesian Network Parameter Estimation Probabilistic Graphical Model Graphical Model Structure Learning Generalized Linear Model Lasso Estimator

January 9, 2023

Distributed Sparse Linear Regression under Communication Constraints
Rodney Fonseca, Boaz Nadler
Sparse Linear Regression Communication Constraint Statistical Task Computational System Sparse Linear Lasso Estimator

July 26, 2022

Variance estimation in graphs with the fused lasso
Oscar Hernan Madrid Padilla
Graph Drawing Standard LASSO Sub Gaussian Graph Problem Heteroscedastic Regression Variance Estimation Lasso Estimator