4 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
Theresa Eimer, Lennart Schäpermeier, André Biedenkapp +15
Empirical research on meta-algorithmics, such as algorithm selection, configuration, and scheduling, often relies on extensive and thus computationally expensive experiments. With…
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
Hilde Weerts, Florian Pfisterer, Matthias Feurer +7
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducin…
Neural Model-based Optimization with Right-Censored Observations
Katharina Eggensperger, Kai Haase, Philipp Müller +2
In many fields of study, we only observe lower bounds on the true response value of some experiments. When fitting a regression model to predict the distribution of the outcomes, w…
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
Katharina Eggensperger, Marius Lindauer, Holger H. Hoos +2
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard com…