Group Lasso

Group Lasso is a regularization technique used in machine learning to select relevant features and improve model interpretability by encouraging sparsity within predefined groups of variables. Current research focuses on improving the efficiency and scalability of Group Lasso algorithms, particularly within complex models like neural networks and transformers, often employing techniques like block-coordinate descent and adaptive noise augmentation. These advancements are impacting diverse fields, including genetics, system identification, and signal processing, by enabling more efficient analysis of high-dimensional data and facilitating the development of more accurate and interpretable predictive models. The development of faster and more robust Group Lasso methods continues to be a significant area of investigation.

Papers

September 29, 2024

NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization
Duy-Tung Pham, Thien Trang Nguyen Vu, Tung Nguyen, Linh Ngo Van, Duc Anh Nguyen, Thien Huu Nguyen
Mutual Information Pretrained Language Model Neural Topic Neural Topic Model Topic Classification Group Lasso

June 30, 2024

Sum-of-norms regularized Nonnegative Matrix Factorization
Andersen Ang, Waqas Bin Hamed, Hans De Sterck
Stable Rank Group Lasso

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

April 26, 2024

Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank
Thomas Le Menestrel, Erin Craig, Robert Tibshirani, Trevor Hastie, Manuel Rivas
Disease Prediction Multi Omics Data Interaction Modeling Group Lasso Genome Wide Association Study United Kingdom Biobank Depth Based Variant Search Algorithm

March 6, 2024

Linear and nonlinear system identification under $\ell_1$- and group-Lasso regularization via L-BFGS-B
Alberto Bemporad
State Space Model Discrete Time System Identification Linear Ordered Data Nonlinear System Identification Group Lasso L BFGS

January 29, 2024

Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
Giovanni S. Alberti, Luca Ratti, Matteo Santacesaria, Silvia Sciutto
LeArning Abstract Inverse Problem Sparsity Increase Group Lasso Sparsity Regularization

August 17, 2023

A comprehensive study of spike and slab shrinkage priors for structurally sparse Bayesian neural networks
Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti
Comprehensive Study Sparse Representation Structured Sparsity Spike Protein Structural Prior Group Lasso Network Sparsity

July 14, 2023

Performance of $\ell_1$ Regularization for Sparse Convex Optimization
Kyriakos Axiotis, Taisuke Yasuda
System Performance Sparse Optimization Strong Convexity Group Lasso Sparse Vector

July 1, 2023

Sparse-Input Neural Network using Group Concave Regularization
Bin Luo, Susan Halabi
Feature Selection Sparse Neural Network Group Lasso Group Sparse

June 2, 2023

Hierarchical Quadratic Random Forest Classifier
Faezeh Fallah
Discriminant Analysis Random Forest Classifier Group Lasso

March 20, 2023

Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing
Nived Rajaraman, Devvrit, Aryan Mokhtari, Kannan Ramchandran
Regularization Model Group Lasso Regularization Based Method Matrix Sensing Meta Pruning

February 4, 2023

Structural Explanations for Graph Neural Networks using HSIC
Ayato Toyokuni, Makoto Yamada
Graph Neural Network Graph Classification Graph Feature GNN Explanation Structured Explanation Group Lasso Hilbert Schmidt Independence Criterion

January 17, 2023

Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data
Louna Alsouki, Laurent Duval, Clément Marteau, Rami El Haddad, François Wahl
Human Prediction Feature Selection Sparse Linear Regression Group Lasso Parameter Efficient Sparsity Partial Least Square

November 26, 2022

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning
Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
Strong Generalization Multi Task Learning Sparsity Increase Disentanglement Capability Novel Identifiability Test Bi Level Optimization Group Lasso Base Editor

November 16, 2022

The non-overlapping statistical approximation to overlapping group lasso
Mingyu Qi, Tianxi Li
Group Lasso Penalty Method

October 27, 2022

Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary
Cristina Butucea, Jean-François Delmas, Anne Dutfoy, Clément Hardy
Mixture Component RSD Difference of Gaussian Group Lasso Finite Mixture Norm Penalty

April 18, 2022

Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models
Yinan Li, Fang Liu
Scientific Inference Generalized Linear Model Regularization Effect Group Lasso Adaptive Lasso Noise Robust Loss

February 25, 2022

Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity
Shiyun Xu, Zhiqi Bu, Pratik Chaudhari, Ian J. Barnett
Feature Selection Additive Model Interpretable Deep Learning Generalized Additive Model Neural Additive Model Group Lasso Group Sparsity Group Sparse

January 22, 2022

glassoformer: a query-sparse transformer for post-fault power grid voltage prediction
Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin
Efficient Transformer Sparse Transformer Group Lasso Standard Transformer Voltage Prediction

Group Lasso

Papers

NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization

Sum-of-norms regularized Nonnegative Matrix Factorization

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

Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank

Linear and nonlinear system identification under $\ell_1$- and group-Lasso regularization via L-BFGS-B

Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems

A comprehensive study of spike and slab shrinkage priors for structurally sparse Bayesian neural networks

Performance of $\ell_1$ Regularization for Sparse Convex Optimization

Sparse-Input Neural Network using Group Concave Regularization

Hierarchical Quadratic Random Forest Classifier

Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing

Structural Explanations for Graph Neural Networks using HSIC

Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning

The non-overlapping statistical approximation to overlapping group lasso

Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary

Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models

Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity

glassoformer: a query-sparse transformer for post-fault power grid voltage prediction