9.4k citations · 16.5k across the 107 of their papers we have counts for
11 papers · 1 filter
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…
Decoupled Weight Decay Regularization
Ilya Loshchilov, Frank Hutter
L regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \em…
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…
Neural Networks for Predicting Algorithm Runtime Distributions
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime,…
Warmstarting of Model-based Algorithm Configuration
Marius Lindauer, Frank Hutter
The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI com…