Lasso Condition Number

Lasso condition number research focuses on understanding and mitigating the instability and computational challenges associated with Lasso regression, a widely used technique for high-dimensional sparse data analysis. Current research investigates the impact of data properties, such as the design matrix's condition number and the presence of adversarial noise, on Lasso's performance and stability, often employing asymptotic analysis and primal-dual witness methods. These studies aim to improve the reliability and efficiency of Lasso-based feature selection and variable selection algorithms, with implications for various fields including machine learning, bioinformatics, and signal processing. A key goal is developing robust algorithms that provide trustworthy results even under challenging data conditions.

Papers

October 5, 2024

Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification
Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed
Graph Neural Network Comparative Study Multi Omics Data Cancer Classification Multi Omics Lasso Condition Number

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

May 5, 2024

Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection
Jingbo Liu
Financial Application Core Stability Variable Selection LASSO Regression Lasso Problem Lasso Condition Number

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

December 21, 2022

A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression
Deepak Maurya, Jean Honorio
Adversarial Attack Mixed Effect Adversarial Perturbation Primal Dual Theoretical Analysis Sparse Regression Robust Algorithm Vector Regression Lasso Condition Number

October 13, 2022

Invariance-adapted decomposition and Lasso-type contrastive learning
Masanori Koyama, Takeru Miyato, Kenji Fukumizu
Contrastive Learning Latent Space Microbial Decomposition Invariant Manifold Lasso Condition Number

April 26, 2022

Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production
Syeda Sakira Hassan, Rahul Mangayil, Tommi Aho, Olli Yli-Harja, Matti Karp
Person Identification Biological Pathway Non Structural Protein Lasso Condition Number

Lasso Condition Number

Papers

Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification

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

Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection

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

A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression

Invariance-adapted decomposition and Lasso-type contrastive learning

Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production