Hyperparameter selection using budget-aware bayesian optimization

Abstract

Generally discussed herein are devices, systems, and methods for identifying optimal hyperparameter values within a pre-defined budget. A method can include, while training a model for a number of iterations using values of a hyperparameter vector, recording objective function values of the objective function and cost function values of a cost function, fitting a function model to the objective function values and a cost model to the cost function values resulting in a fitted function model and a fitted cost model, selecting a second hyperparameter vector, determining an optimal number of iterations to perform and after which to stop training using the second hyperparameter vector, re-training the model of the type of model for the optimal number of iterations using the second hyperparameter vector, and providing hyperparameter values, of the hyperparameter vector or the second hyperparameter vector, that maximize an objective defined by the objective function.