Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization
cs.LG, cs.RO, cs.SY, eess.SY
Submitted: 2026-03-27
Updated: 2026-09-16
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: We propose an Expected Free Energy-based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e., optimize and learn the underlying function
Terminology
Abstract
We propose an Expected Free Energy-based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e., optimize and learn the underlying function simultaneously. We show that, under specific assumptions, Expected Free Energy reduces to Upper Confidence Bound, Lower Confidence Bound, and Expected Information Gain. We prove that Expected Free Energy has unbiased convergence guarantees for concave functions. Using the results from these derivations, we introduce a curvature-aware update law for Expected Free Energy and show its proof of concept using a system identification problem on a Van der Pol oscillator. On a two-dimensional benchmark with an oscillatory landscape, our adaptive Expected Free Energy acquisition achieves competitive performance in both regret and mean squared error, unlike the typical acquisition functions that perform well in only one metric.
Sources
- A Tutorial on Bayesian Optimization
- Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
- Beyond Objective-Based Improvement: Stationarity-Aware Expected Improvement for Bayesian Optimization
- Pragmatic Curiosity: A Unified Framework for Hybrid Learning and Optimization via Active Inference
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