3 papers
cs.LG2019
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
Marius Lindauer, Matthias Feurer, Katharina Eggensperger +2
Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning. Although it is well-accepted that HPO is crucial to obtain well…
cs.LG2019
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
Marius Lindauer, Katharina Eggensperger, Matthias Feurer +4
Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a s…
cs.LG2019
Towards White-box Benchmarks for Algorithm Control
André Biedenkapp, H. Furkan Bozkurt, Frank Hutter +1
The performance of many algorithms in the fields of hard combinatorial problem solving, machine learning or AI in general depends on tuned hyperparameter configurations. Automated…