Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
This work addresses the lack of joint NAS and HPO methods for multi-objective optimization, which is an incremental step for researchers in automated machine learning.
The paper tackles the problem of jointly optimizing neural architectures and hyperparameters for multiple objectives, such as performance and resource requirements, by proposing a set of methods that extend current approaches to serve as baselines for future research.
Neural architecture search (NAS) and hyperparameter optimization (HPO) make deep learning accessible to non-experts by automatically finding the architecture of the deep neural network to use and tuning the hyperparameters of the used training pipeline. While both NAS and HPO have been studied extensively in recent years, NAS methods typically assume fixed hyperparameters and vice versa - there exists little work on joint NAS + HPO. Furthermore, NAS has recently often been framed as a multi-objective optimization problem, in order to take, e.g., resource requirements into account. In this paper, we propose a set of methods that extend current approaches to jointly optimize neural architectures and hyperparameters with respect to multiple objectives. We hope that these methods will serve as simple baselines for future research on multi-objective joint NAS + HPO. To facilitate this, all our code is available at https://github.com/automl/multi-obj-baselines.