mlsanityData to 14 Oct 2026

Max-value Entropy Search for Efficient Bayesian Optimization

arXiv 1703.01968 ↗First posted 6 Mar 2017stat.ML cs.LG math.OCcited by 206

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Abstract (arXiv)

Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information gained about the argmax of the unknown function; yet, both are plagued by the expensive computation for estimating entropies. We propose a new criterion, Max-value Entropy Search (MES), that instead uses the information about the maximum function value. We show relations of MES to other Bayesian optimization methods, and establish a regret bound. We observe that MES maintains or improves the good empirical performance of ES/PES, while tremendously lightening the computational burden. In particular, MES is much more robust to the number of samples used for computing the entropy, and hence more efficient for higher dimensional problems.

Key facts. Cited by 206 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Mar 2017.

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Citing papers per year: 2021: 36; 2022: 37; 2023: 38; 2024: 36; 2025: 33; 2026: 26.

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38 more
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  12. 2021Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Boundcited by 23
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  29. 2023Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Boundscited by 16
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  32. 2022Meta-Learning Priors for Safe Bayesian Optimizationcited by 14
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  38. 2021Lenient Regret and Good-Action Identification in Gaussian Process Banditscited by 12

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