9.4k citations · 16.5k across the 107 of their papers we have counts for
4 papers · 2 filters
The reparameterization trick for acquisition functions
James T. Wilson, Riccardo Moriconi, Frank Hutter +1
Bayesian optimization is a sample-efficient approach to solving global optimization problems. Along with a surrogate model, this approach relies on theoretically motivated value he…
Simple And Efficient Architecture Search for Convolutional Neural Networks
Thomas Elsken, Jan-Hendrik Metzen, Frank Hutter
Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cum…
Hyperparameter Importance Across Datasets
J. N. van Rijn, F. Hutter
With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by…
OpenML Benchmarking Suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer +6
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine l…