Variational Entropy Search for Adjusting Expected Improvement
<|reference_start|>Variational Entropy Search for Adjusting Expected Improvement: 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.<|reference_end|>
@article{cheng2024variational,
    title={Variational Entropy Search for Adjusting Expected Improvement},
    author={Nuojin Cheng and Stephen Becker},
    journal={arXiv preprint arXiv:2402.11345},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11345},
    primaryClass={stat.ML cs.LG math.OC}
}