Abstract This callback implements an automatic stopping mechanism for Optuna studies, aiming to avoid unnecessary computation. The optimization is terminated when the statistical error of the objective function (e.g., cross-validation error) exceeds the room left for optimization (i.e., the estimated potential for improvement).
The mechanism is described in the following papers:
A. Makarova et al. Automatic termination for hyperparameter optimization. <https://proceedings.mlr.press/v188/makarova22a.html>__ H. Ishibashi et al. A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets.