Sparse Logistic Regression

Sparse logistic regression is a statistical method used for classification and simultaneous feature selection, aiming to identify the most relevant predictors while producing a parsimonious model. Current research focuses on developing efficient algorithms, particularly for high-dimensional data and non-convex regularization terms, including advancements in accelerated proximal methods and primal-dual approaches to improve computational speed and convergence guarantees. These improvements enhance the applicability of sparse logistic regression across diverse fields, from natural language processing (e.g., grammar rule extraction) to bioinformatics (e.g., gene selection), by enabling the analysis of large datasets with many features while maintaining model interpretability.

Papers

March 26, 2024

Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks
Santiago Herrera, Caio Corro, Sylvain Kahane
Fine Grained Corpus Based Dependency Treebanks Syntactic Rule Grammar Description Order Feature Sparse Logistic Regression

September 17, 2023

Globally Convergent Accelerated Algorithms for Multilinear Sparse Logistic Regression with $\ell_0$-constraints
Weifeng Yang, Wenwen Min
Low Rank Tensor Accelerated Gradient Method Multilinear Discriminant Analysis Sparse Logistic Regression

September 12, 2023

On Regularized Sparse Logistic Regression
Mengyuan Zhang, Kai Liu
Feature Selection Logistic Regression Nonconvex Regularization Sparse Logistic Regression

April 24, 2023

Sparse Private LASSO Logistic Regression
Amol Khanna, Fred Lu, Edward Raff, Brian Testa
Many Sparse Logistic Regression Standard LASSO Private Linear Regression Adaptive Lasso Sparse Logistic Regression

March 2, 2023

Variational EP with Probabilistic Backpropagation for Bayesian Neural Networks
Kehinde Olobatuyi
Bayesian Neural Network Category Wise Variation Probabilistic Neural Network Expectation Propagation Hierarchical Bayesian Model Sparse Logistic Regression

May 29, 2022

A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension
Binh T. Nguyen, Bertrand Thirion, Sylvain Arlot
High Dimension Logistic Regression Exact Inference High Dimensional Problem Sparse Logistic Regression Conditional Randomization

April 13, 2022

Generalization Error Bounds for Multiclass Sparse Linear Classifiers
Tomer Levy, Felix Abramovich
High Dimensional Generalization Error Bound Sparsity Constraint Sparse Logistic Regression

February 23, 2022

Fast Sparse Classification for Generalized Linear and Additive Models
Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin
Many Sparse Interpretable Model Generalized Linear Model Additive Model Fast Classification Sparse Classification Sparse Logistic Regression

November 30, 2021

Efficient and robust high-dimensional sparse logistic regression via nonlinear primal-dual hybrid gradient algorithms
Jérôme Darbon, Gabriel P. Langlois
Logistic Regression Variable Selection Sparse Logistic Regression