There are 3 repositories under sparse-regression topic.
A package for the sparse identification of nonlinear dynamical systems from data
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
Statistical Models with Regularization in Pure Julia
Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.
Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.
Variable Selection and Task Grouping for Multi-Task Learning (VSTG-MTL)
Black-box spike and slab variational inference, example with linear models
locus R package - Large-scale variational inference for variable selection in sparse multiple-response regression
Actually Sparse Variational Gaussian Processes implemented in GPlow
Sparse Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modified Differential Equations
Physically-informed model discovery of systems with nonlinear, rational terms using the SINDy-PI method. Contains functionality for spectral filtering/differentiation.
Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.
Sparse Bayesian ARX models with flexible noise distributions
Robust regression algorithm that can be used for explaining black box models (Python implementation)
Robust regression algorithm that can be used for explaining black box models (R implementation)
The official respository for noise-aware physics-informed machine learning (nPIML)
This work presents the application of machine learning models in order to obtain a sparse governing equation of complex fluid dynamics problems.
Generalized Orthogonal Least-Squares in CUDA
Automatic hyperparameter selection for Lasso-like models solving the M/EEG source localization problem
Methods for data segmentation under a sparse regression model
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal Society A.
Horseshoe regression model fitted in PyMC.
Physics-informed refinement learning for equation discovery
This repository stores my personal projects related to data science studies.
The Method of Entropic Regression, sparse system identification method based on causality inference of complex networks.
Simple implementation of (Takada & Fujisawa, 2020, NeurIPS) and (Takada & Fujisawa, 2023, arXiv)
A Python Package for a Sparse Additive Boosting Regressor