A package for the sparse identification of nonlinear dynamical systems from data
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Updated
May 8, 2024 - Python
A package for the sparse identification of nonlinear dynamical systems from data
Efficient Algorithms for L0 Regularized Learning
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
a collection of modern sparse (regularized) linear regression algorithms.
Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)
Matlab toolbox for sparse regression
Sorted L1 Penalized Estimation
MGLM Toolbox for Matlab
Statistical Models with Regularization in Pure Julia
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)
STELA algorithm for sparsity regularized linear regression (LASSO)
Hybrid Approach to Sparse Group Fused Lasso
Sparse Bayesian ARX models with flexible noise distributions
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 Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modified Differential Equations
locus R package - Large-scale variational inference for variable selection in sparse multiple-response regression
Robust regression algorithm that can be used for explaining black box models (R implementation)
Methods for data segmentation under a sparse regression model
Robust regression algorithm that can be used for explaining black box models (Python implementation)
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