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Deep learning models are full of hyperparameters, which are set manuallybefore the learning process can start. To find the best configuration for thesehyperparameters in such a high dimensional space, with time-consuming andexpensive model training / validation, is not a trivial challenge. Bayesianoptimization is a powerful tool for the joint optimization of hyperparameters,efficiently trading off exploration and exploitation of the hyperparameterspace. In this paper, we discuss Bayesian hyperparameter optimization,including hyperparameter optimization, Bayesian optimization, and Gaussianprocesses. We also review BoTorch, GPyTorch and Ax, the new open-sourceframeworks that we use for Bayesian optimization, Gaussian process inferenceand adaptive experimentation, respectively. For experimentation, we applyBayesian hyperparameter optimization, for optimizing group weights, to weightedgroup pooling, which couples unsupervised tiered graph autoencoders learningand supervised graph prediction learning for molecular graphs. We find that Ax,BoTorch and GPyTorch together provide a simple-to-use but powerful frameworkfor Bayesian hyperparameter optimization, using Ax's high-level API thatconstructs and runs a full optimization loop and returns the besthyperparameter configuration.

Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax