Details
| Original language | English |
|---|---|
| Number of pages | 8 |
| Journal | Journal of Machine Learning Research |
| Volume | 2022 |
| Issue number | 23 |
| Publication status | Published - Feb 2022 |
Abstract
Keywords
- cs.LG, stat.ML
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In: Journal of Machine Learning Research, Vol. 2022, No. 23, 02.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - SMAC3
T2 - A Versatile Bayesian Optimization Package for Hyperparameter Optimization
AU - Lindauer, Marius
AU - Eggensperger, Katharina
AU - Feurer, Matthias
AU - Biedenkapp, André
AU - Deng, Difan
AU - Benjamins, Carolin
AU - Sass, René
AU - Hutter, Frank
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - cs.LG
KW - stat.ML
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85124235402&origin=inward&txGid=d1c777c5ed2d26cb3a80c55de04b9e80
M3 - Article
VL - 2022
JO - Journal of Machine Learning Research
JF - Journal of Machine Learning Research
SN - 1532-4435
IS - 23
ER -