Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization
Read the original on arXiv Machine Learning →The paper introduces a curvature‑aware Expected Free Energy (EFE) acquisition function for Bayesian optimization, designed to jointly learn and optimize an underlying function. It demonstrates that, under certain assumptions, EFE reduces to familiar criteria such as Upper Confidence Bound, Lower Confidence Bound, and Expected Information Gain, and provides unbiased convergence guarantees for concave functions. Empirical results on a Van der Pol oscillator system identification task and a two‑dimensional oscillatory benchmark show that the adaptive EFE achieves competitive performance in both regret and mean squared error, outperforming typical acquisition functions that excel in only one metric.
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