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Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection
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“GP and RFR, when used as the hypothetical value providers, balance exploitation and exploration, whereas GP_CLmax and GP_CLmin emphasize exploitation and exploration, respectively49.”
From Data-driven de novo design of super-adhesive hydrogels (2025) · cites this paper as [49]
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References 24 · Cited by 147
Research works printed but not linked: 2
- Mockus, J., Tiesis, V., Zilinskas, A.: The application of Bayesian methods for seeking the extremum. In: Dixon, L., Szego, E.G. (eds.) Towards Global Optimization, pp. 117–129. Elsevier, Amsterdam (1978)
- Frazier, P.I.: Parallel global optimization using an improved multi-points expected improvement criterion. In: INFORMS Optimization Society Conference, Miami FL (2012)
Metadata
- Authors
- Chevalier, C., & Ginsbourger, D.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Ginsbourger, David
- ORCID
- Ginsbourger, David
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 7997
- Pages
- 59-69
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W61667912
- Cited by
- 147
- References
- 24
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 13 · 2023: 15 · 2022: 18 · 2021: 14 · 2020: 18 · 2019: 14 · 2018: 8 · 2017: 12 · 2016: 4 · 2015: 4 · 2014: 2 · 2013: 2
- FWCI
- 6.73
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- University of Bern
- Institut national de recherche en informatique et en automatique (INRIA)
- IFP Energies Nouvelles
- Institut de Radioprotection et de SÛreté Nucléaire
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- batch Bayesian optimization, batch selection, closed-form expressions, surrogate-based optimization
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection. https://app.cochat.ai/papers/10.1007/978-3-642-44973-4_7
- The paper
- Chevalier, C., & Ginsbourger, D. (2013). Fast Computation of the Multi-Points Expected Improvement with Applications in Batch Selection. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-44973-4_7
