Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
This work addresses hyperparameter tuning for researchers in machine learning, particularly in graph-based applications, but it is incremental as it primarily reviews and applies existing methods with new frameworks.
The paper tackles the challenge of hyperparameter optimization in deep learning by applying Bayesian optimization using the BoTorch, GPyTorch, and Ax frameworks to optimize group weights in weighted group pooling for molecular graph tasks, finding that these tools provide a simple yet powerful framework for this purpose.
Deep learning models are full of hyperparameters, which are set manually before the learning process can start. To find the best configuration for these hyperparameters in such a high dimensional space, with time-consuming and expensive model training / validation, is not a trivial challenge. Bayesian optimization is a powerful tool for the joint optimization of hyperparameters, efficiently trading off exploration and exploitation of the hyperparameter space. In this paper, we discuss Bayesian hyperparameter optimization, including hyperparameter optimization, Bayesian optimization, and Gaussian processes. We also review BoTorch, GPyTorch and Ax, the new open-source frameworks that we use for Bayesian optimization, Gaussian process inference and adaptive experimentation, respectively. For experimentation, we apply Bayesian hyperparameter optimization, for optimizing group weights, to weighted group pooling, which couples unsupervised tiered graph autoencoders learning and supervised graph prediction learning for molecular graphs. We find that Ax, BoTorch and GPyTorch together provide a simple-to-use but powerful framework for Bayesian hyperparameter optimization, using Ax's high-level API that constructs and runs a full optimization loop and returns the best hyperparameter configuration.