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CONTROL OF HYPERPARAMETER TUNING BASED ON MACHINE LEARNING

Abstract:

Systems, methods, articles of manufacture, and computer program products to train a generation model to determine whether a search space portion is likely to provide hyperparameters that improve a success metric; sequentially select at least a subset of multiple search space portions; for each selected search space portion, generate hyperparameters from the search space portion, perform hyperparameter tuning with the hyperparameters to determine whether the hyperparameters improved the success metric, apply the generation model based on whether the success metric is improved to determine whether the search space portion is likely to provide further hyperparameters that improve the success metric, and rule out the search space portion from providing further hyperparameters in response to determining that the search space portion is unlikely to provide further hyperparameters that improve the success metric; and terminate the performance of hyperparameter tuning when all search space portions are ruled out.