Variational Entropy Search for Adjusting Expected Improvement

• URL: http://arxiv.org/abs/2402.11345v1
• Date: Sat, 17 Feb 2024 17:37:53 GMT
• Title: Variational Entropy Search for Adjusting Expected Improvement
• Authors: Nuojin Cheng and Stephen Becker
• Abstract summary: Expected Improvement (EI) is the most commonly utilized acquisition function in black-box functions. We have developed the Variational Entropy Search (VES) methodology and the VES-Gamma algorithm, which adapts EI by incorporating principles from information-theoretic concepts.
• Score: 3.04585143845864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization is a widely used technique for optimizing black-box functions, with Expected Improvement (EI) being the most commonly utilized acquisition function in this domain. While EI is often viewed as distinct from other information-theoretic acquisition functions, such as entropy search (ES) and max-value entropy search (MES), our work reveals that EI can be considered a special case of MES when approached through variational inference (VI). In this context, we have developed the Variational Entropy Search (VES) methodology and the VES-Gamma algorithm, which adapts EI by incorporating principles from information-theoretic concepts. The efficacy of VES-Gamma is demonstrated across a variety of test functions and read datasets, highlighting its theoretical and practical utilities in Bayesian optimization scenarios.

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