Supervising the Multi-Fidelity Race of Hyperparameter Configurations

• URL: http://arxiv.org/abs/2202.09774v2
• Date: Thu, 1 Jun 2023 08:55:35 GMT
• Title: Supervising the Multi-Fidelity Race of Hyperparameter Configurations
• Authors: Martin Wistuba, Arlind Kadra, Josif Grabocka
• Abstract summary: We introduce DyHPO, a Bayesian Optimization method that learns to decide which hyperparameter configuration to train further in a race among all feasible configurations. We demonstrate the significant superiority of DyHPO against state-of-the-art hyperparameter optimization methods through large-scale experiments.
• Score: 22.408069485293666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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