GENO - Optimization for Classical Machine Learning Made Fast and Easy
Abstract
Most problems from classical machine learning can be cast as an optimization problem. We introduce GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver, i.e., Python code, that can solve this class of optimization problems. The generated solver is usually as fast as hand-written, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific problem class.An online interface to our framework can be found at http://www.geno-project.org.
Cite
Text
Laue et al. "GENO - Optimization for Classical Machine Learning Made Fast and Easy." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I09.7097Markdown
[Laue et al. "GENO - Optimization for Classical Machine Learning Made Fast and Easy." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/laue2020aaai-geno/) doi:10.1609/AAAI.V34I09.7097BibTeX
@inproceedings{laue2020aaai-geno,
title = {{GENO - Optimization for Classical Machine Learning Made Fast and Easy}},
author = {Laue, Sören and Mitterreiter, Matthias and Giesen, Joachim},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13620-13621},
doi = {10.1609/AAAI.V34I09.7097},
url = {https://mlanthology.org/aaai/2020/laue2020aaai-geno/}
}