Efficient Algorithms for L0 Regularized Learning
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021
Learn how to use L0 regularization
The L0-SIGN implementation.
An Exact L0-penalized Problem Solver.
R package for fitting L0-norm penalized generalized linear models (GLMs) and group-L0 penalized multichannel GLMs. It is aimed at best subset selection and best subset of group selection in low- and high-dimensional settings. Applications include sparse signal deconvolution, such as overlapping peak recovery in GC/MS chromatography.
A header-only C++ library for solving Sparse Modeling Problems via Biconjugate Convex Relaxation Techniques.
A hands-on guide to model selection, emphasizing high-dimensional problems, Bayesian model selection and averaging, and L0 criteria
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