252 citations · 293 across the 6 of their papers we have counts for
9 papers · 1 filter
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…
Model-based Asynchronous Hyperparameter and Neural Architecture Search
Aaron Klein, Louis C. Tiao, Thibaut Lienart +2
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian p…
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
Matilde Gargiani, Aaron Klein, Stefan Falkner +1
We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based…
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization
Aaron Klein, Frank Hutter
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate…
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Aaron Klein, Zhenwen Dai, Frank Hutter +2
Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are…