Multiple Information Source Bayesian Optimization
The book provides a comprehensive review of multiple information sources and multi-fidelity Bayesian optimization, specifically focusing on the novel "Augmented Gaussian Process” methodology. The book is important to clarify the relations and the important differences in using multi-fidelity or...
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| Auteurs principaux: | , , |
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| Collectivité auteur: | |
| Format: | Électronique eBook |
| Langue: | anglais |
| Publié: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Édition: | 1st ed. 2025. |
| Collection: | SpringerBriefs in Optimization,
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| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-3-031-97965-1 |
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| 100 | 1 | |a Candelieri, Antonio. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Multiple Information Source Bayesian Optimization |h [electronic resource] / |c by Antonio Candelieri, Andrea Ponti, Francesco Archetti. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XII, 99 p. 30 illus.). |b 27 illus. in color. | ||
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| 505 | 0 | |a Preface -- Introduction -- MISO-AGP: dealing with multiple information sources via Augmented Gaussian Process -- MISO-AGP in action: selected applications -- Bayesian Optimization and Large Language Models -- References. | |
| 520 | |a The book provides a comprehensive review of multiple information sources and multi-fidelity Bayesian optimization, specifically focusing on the novel "Augmented Gaussian Process” methodology. The book is important to clarify the relations and the important differences in using multi-fidelity or multiple information source approaches for solving real-world problems. Choosing the most appropriate strategy, depending on the specific problem features, ensures the success of the final solution. The book also offers an overview of available software tools: in particular it presents two implementations of the Augmented Gaussian Process-based Multiple Information Source Bayesian Optimization, one in Python -- and available as a development branch in BoTorch -- and finally, a comparative analysis against other available multi-fidelity and multiple information sources optimization tools is presented, considering both test problems and real-world applications. The book will be useful to two main audiences: 1. PhD candidates in Computer Science, Artificial Intelligence, Machine Learning, and Optimization 2. Researchers from academia and industry who want to implement effective and efficient procedures for designing experiments and optimizing computationally expensive experiments in domains like engineering design, material science, and biotechnology. . | ||
| 532 | 8 | |a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility. It includes screen reader support, descriptions for Non-text content (such as images and graphs), bookmarks for easy navigation, keyboard-friendly links and forms, and searchable, selectable text. We recognise the importance of accessibility and welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us via https://www.springernature.com/gp/info/accessibility Please Note that a more accessible version of this eBook is available as an EPUB. | |
| 532 | 8 | |a Inaccessible, or known limited accessibility | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Machine learning. | |
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| 650 | 2 | 4 | |a Bayesian Inference. |
| 650 | 2 | 4 | |a Machine Learning. |
| 700 | 1 | |a Ponti, Andrea. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Archetti, Francesco. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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