83 citations · 98 across the 4 of their papers we have counts for
4 papers
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
René Sass, Eddie Bergman, André Biedenkapp +2
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pip…
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…
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…
Risk Assessment for Machine Learning Models
Paul Schwerdtner, Florens Greßner, Nikhil Kapoor +5
In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment. For that we carry over the risk definitio…