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A network-based optimization approach, EEE, is proposed for the purpose ofproviding validation-viable state estimations to remediate the failure ofpretrained models. To improve optimization efficiency and convergence, the mostimportant metrics in the context of this research, we follow a three-facetedapproach based on the error from the validation process. Firstly, we improvethe information content of the error by designing a validation module toacquire high-dimensional error information. Next, we reduce the uncertainty oferror transfer by employing an ensemble of error estimators, which only learnimplicit errors, and use Constrained Ensemble Exploration to collect high-valuedata. Finally, the effectiveness of error utilization is improved by usingensemble search to determine the most prosperous state. The benefits of theproposed framework are demonstrated on four real-world engineering problemswith diverse state dimensions. It is shown that EEE is either as competitive oroutperforms popular optimization methods, in terms of efficiency andconvergence.

EEE, Remediating the failure of machine learning models via a network-based optimization patch