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Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Martin Wistuba · Arlind Kadra · Josif Grabocka

Wed Dec 07 09:00 AM -- 11:00 AM (PST) @
in Lightning Talks 3B-1 »

Multi-fidelity (gray-box) hyperparameter optimization techniques (HPO) have recently emerged as a promising direction for tuning Deep Learning methods. However, existing methods suffer from a sub-optimal allocation of the HPO budget to the hyperparameter configurations. In this work, we introduce DyHPO, a Bayesian Optimization method that learns to decide which hyperparameter configuration to train further in a dynamic race among all feasible configurations. We propose a new deep kernel for Gaussian Processes that embeds the learning curve dynamics, and an acquisition function that incorporates multi-budget information. We demonstrate the significant superiority of DyHPO against state-of-the-art hyperparameter optimization methods through large-scale experiments comprising 50 datasets (Tabular, Image, NLP) and diverse architectures (MLP, CNN/NAS, RNN).

Keywords: hyperparameter optimization

Author Information

Martin Wistuba (Amazon)
Arlind Kadra (University of Freiburg)
Josif Grabocka (Universität Freiburg)

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