Standard LASSO

Standard LASSO (Least Absolute Shrinkage and Selection Operator) is a regression technique that performs variable selection and regularization by shrinking coefficients towards zero using an L1 penalty. Current research focuses on improving LASSO's efficiency and robustness, including developing faster algorithms (like those based on block-coordinate descent), addressing challenges posed by high dimensionality and correlated covariates (through techniques such as rescaling and latent variable modeling), and enhancing its applicability in various contexts such as multi-omics data analysis and causal inference (e.g., estimating conditional average treatment effects). The widespread use of LASSO across diverse fields, from genomics to imaging, highlights its significance in extracting meaningful insights from high-dimensional data and improving the accuracy and interpretability of predictive models.

Papers

October 11, 2024

Test-driven Software Experimentation with LASSO: an LLM Benchmarking Example
Marcus Kessel
Single Example LLM Benchmark Standard LASSO Automatic Analysis Code Semantics Software Engineering Research Driven Development

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

September 26, 2024

Transfer Learning in $\ell_1$ Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis
Koki Okajima, Tomoyuki Obuchi
Transfer Learning Generalization Performance Hyperparameter Selection Standard LASSO Lasso Condition Number

August 30, 2024

LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification
Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed
Multi Omics Data Cancer Classification Gene Expression Data Standard LASSO

August 20, 2024

Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso
Yelleti Vivek, Sri Krishna Vadlamani, Vadlamani Ravi, P. Radha Krishna
Feature Selection Quantum Physic Metaheuristic Algorithm Differential Evolution Kill Chaos Standard LASSO Chaotic Behavior Simultaneous Improvement Quantum Inspired

July 22, 2024

U-learning for Prediction Inference via Combinatory Multi-Subsampling: With Applications to LASSO and Neural Networks
Zhe Fei, Yi Li
Neural Network Predictive Inference Standard LASSO Ensemble Prediction Universal Learning DNA Methylation Biological Age Epigenetic State Input

July 18, 2024

With or Without Replacement? Improving Confidence in Fourier Imaging
Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer, Marion I. Menzel, Holger Rauhut
Uncertainty Quantification High Confidence Gaussian Model Standard LASSO Gaussian Data Frequency Imaging

May 27, 2024

Dual feature reduction for the sparse-group lasso and its adaptive variant
Fabio Feser, Marina Evangelou
Standard LASSO Group Lasso Feature Reduction Sparse Penalty

May 14, 2024

A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent
James Yang, Trevor Hastie
Standard LASSO Minimal Path Block Coordinate Descent Group Lasso LASSO Regression Regularization Path

February 23, 2024

Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps
Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Sparse Linear Regression Sparse Principal Component Analysis Standard LASSO Efficient Estimation Situated Latents Computational Gap Word Level Rescaling Asset Correlation Localized Sparse Incomplete Sparse Parity

February 4, 2024

A Fast Method for Lasso and Logistic Lasso
Siu-Wing Cheng, Man Ting Wong
Compressed Sensing Standard LASSO LASSO Regression Sparse Reconstruction

December 18, 2023

When can you trust feature selection? -- I: A condition-based analysis of LASSO and generalised hardness of approximation
Alexander Bastounis, Felipe Cucker, Anders C. Hansen
Feature Selection Average Approximation Ill Posed Inverse Problem Standard LASSO Lasso Problem Lasso Condition Number Condition Assessment Model Hardness Assumption

November 1, 2023

Fixed-Budget Best-Arm Identification in Sparse Linear Bandits
Recep Can Yavas, Vincent Y. F. Tan
High Dimensional Best Arm Identification Standard LASSO Sparse Linear Bandit

October 25, 2023

CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression
Masahiro Kato, Masaaki Imaizumi
High Dimensional Causal Inference Causal Effect Conditional Average Treatment Effect Standard LASSO LASSO Regression

September 7, 2023

Neural lasso: a unifying approach of lasso and neural networks
David Delgado, Ernesto Curbelo, Danae Carreras
Neural Network Machine Learning Cross Validation Variable Selection Unifying Theory Standard LASSO LASSO Regression Statistical Framework

August 8, 2023

Varying-coefficients for regional quantile via KNN-based LASSO with applications to health outcome study
Seyoung Park, Eun Ryung Lee, Hyokyoung G. Hong
Financial Application Quantile Regression Standard LASSO Health Outcome Varying Coefficient Age Based Metric

June 22, 2023

Outlier-robust Estimation of a Sparse Linear Model Using Invexity
Adarsh Barik, Jean Honorio
Sparse Linear Regression Standard LASSO Outlier Sample Lasso Problem Sparse Linear Outlier Robust

June 5, 2023

Improved Active Multi-Task Representation Learning via Lasso
Yiping Wang, Yifang Chen, Kevin Jamieson, Simon S. Du
Representation Learning Multi Task Standard LASSO Target Source

May 17, 2023

On Consistency of Signatures Using Lasso
Xin Guo, Ruixun Zhang, Chaoyi Zhao
Strong Consistency Standard LASSO Handwritten Signature Stochastic Model LASSO Regression Signature Transform

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

Standard LASSO

Papers

Test-driven Software Experimentation with LASSO: an LLM Benchmarking Example

Minmax Trend Filtering: A Locally Adaptive Nonparametric Regression Method via Pointwise Min Max Optimization

Transfer Learning in $\ell_1$ Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis

LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification

Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso

U-learning for Prediction Inference via Combinatory Multi-Subsampling: With Applications to LASSO and Neural Networks

With or Without Replacement? Improving Confidence in Fourier Imaging

Dual feature reduction for the sparse-group lasso and its adaptive variant

A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent

Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps

A Fast Method for Lasso and Logistic Lasso

When can you trust feature selection? -- I: A condition-based analysis of LASSO and generalised hardness of approximation

Fixed-Budget Best-Arm Identification in Sparse Linear Bandits

CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression

Neural lasso: a unifying approach of lasso and neural networks

Varying-coefficients for regional quantile via KNN-based LASSO with applications to health outcome study

Outlier-robust Estimation of a Sparse Linear Model Using Invexity

Improved Active Multi-Task Representation Learning via Lasso

On Consistency of Signatures Using Lasso

Provable Identifiability of Two-Layer ReLU Neural Networks via LASSO Regularization