252 citations · 751 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints
Jan Sosulski, David Hübner, Aaron Klein +1
The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experi…
Automatic Termination for Hyperparameter Optimization
Anastasia Makarova, Huibin Shen, Valerio Perrone +5
Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configuration…
Hyperparameter Transfer Learning with Adaptive Complexity
Samuel Horváth, Aaron Klein, Peter Richtárik +1
Bayesian optimization (BO) is a sample efficient approach to automatically tune the hyperparameters of machine learning models. In practice, one frequently has to solve similar hyp…
BORE: Bayesian Optimization by Density-Ratio Estimation
Louis C. Tiao, Aaron Klein, Matthias Seeger +3
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion enco…