83 citations · 98 across the 4 of their papers we have counts for
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cs.LG2021★ 83 cited
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer +6
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hype…
cs.LG2021★ 9 cited
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik +6
To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications. Over the last years, the number of effici…