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New Bayesian Optimization Method Uses Expected Free Energy

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new acquisition function for Bayesian optimization called Curvature-aware Expected Free Energy. This function aims to solve the joint learning and optimization problem by simultaneously optimizing and learning the underlying function. Under certain assumptions, it can reduce to existing methods like Upper Confidence Bound and Expected Information Gain, and it has been shown to provide unbiased convergence guarantees for concave functions. The proposed method demonstrates competitive performance in both regret and mean squared error on benchmark tests. AI

RANK_REASON The cluster contains a research paper detailing a new acquisition function for Bayesian optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Bayesian Optimization Method Uses Expected Free Energy

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The cluster contains a research paper detailing a new acquisition function for Bayesian optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ajith Anil Meera, Wouter Kouw ·

    Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization

    arXiv:2603.26339v2 Announce Type: replace 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 s…

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