Tim Nonet's scientific contributions

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Publications (1)

The mean and standard error of the following performance metrics: running time in seconds, prediction error (PE), number of false positives (FP), and support size (SS).
L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
  • Preprint
  • File available

February 2022

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130 Reads

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Rahul Mazumder

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Tim Nonet

We introduce L0Learn: an open-source package for sparse regression and classification using L0 regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combinatorial optimization. The package is built using C++ and has a user-friendly R interface. Our experiments indicate that L0Learn can scale to problems with millions of features, achieving competitive run times with state-of-the-art sparse learning packages. L0Learn is available on both CRAN and GitHub.

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