Expected free energy acquisition function self-reports competitive regret and MSE
Meera and Kouw propose an expected free energy acquisition function for joint Bayesian optimization and learning, self-reporting competitive regret and MSE on a 2D oscillator benchmark.
ImportanceLocalEvidenceE2 unreplicated
Bayesian optimization now has a curvature-aware expected free energy acquisition function whose authors self-report competitive performance on both regret and mean squared error on a two-dimensional oscillator benchmark, where typical acquisition functions usually excel at only one.
Previously, Meera and Kouw proposed this acquisition function for the joint problem of optimizing and learning a function in parallel, claiming that under specific assumptions the objective reduces to UCB, LCB, or expected information gain, and proving an unbiased convergence guarantee for concave functions, which yields a curvature-aware update rule.
The empirical evidence is author-reported: a proof of concept using Van der Pol oscillator system identification, with self-reported competitive regret and mean squared error on the two-dimensional oscillator benchmark.
Boundary: the preprint (arXiv 2603.26339, first submitted March 27, revised to v2 on September 16) reports author-run experiments only, with no third-party replication yet.