SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

2021/09/30 by Marius Lindauer, Katharina Eggensperger, Matthias Feurer +6 · 2 citations
Computer Science · Mathematics · #cs.LG #stat.ML

paper · pdf

published as Journal of Machine Learning Research 23 (2022) 1-9

arxiv created 2022/02/08 · arxiv updated 2022/02/09

Abstract

Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at https://github.com/automl/SMAC3.

Cited by

• COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation
• GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs
• CatCMA with Margin: Stochastic Optimization for Continuous, Integer, and Categorical Variables