Initializing Bayesian Hyperparameter Optimization via Meta-Learning

  title={Initializing Bayesian Hyperparameter Optimization via Meta-Learning},
  author={Matthias Feurer and Jost Tobias Springenberg and Frank Hutter},
Model selection and hyperparameter optimization is crucial in applying machine learning to a novel dataset. Recently, a subcommunity of machine learning has focused on solving this problem with Sequential Model-based Bayesian Optimization (SMBO), demonstrating substantial successes in many applications. However, for computationally expensive algorithms the overhead of hyperparameter optimization can still be prohibitive. In this paper we mimic a strategy human domain experts use: speed up… CONTINUE READING
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