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Bayesian Optimization and Data Science de Francesco Archetti

Bayesian Optimization and Data Science: SpringerBriefs in Optimization

Autor Francesco Archetti, Antonio Candelieri
en Limba Engleză Paperback – 7 oct 2019
This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. 
The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.

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Specificații

ISBN-13: 9783030244934
ISBN-10: 3030244938
Pagini: 140
Ilustrații: XIII, 126 p. 52 illus., 39 illus. in color.
Dimensiuni: 155 x 235 x 8 mm
Greutate: 0.21 kg
Ediția:1st ed. 2019
Editura: Springer
Colecția SpringerBriefs in Optimization
Seria SpringerBriefs in Optimization

Locul publicării:Cham, Switzerland

Cuprins

1. Automated Machine Learning and Bayesian Optimization.- 2. From Global Optimization to Optimal Learning.- 3. The Surrogate Model.- 4. The Acquisition Function.- 5. Exotic BO.- 6. Software Resources.- 7. Selected Applications.

Textul de pe ultima copertă

This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems.  The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.

Caracteristici

Gives readers an idea of the potential of the application of Bayesian Optimization to both traditional feels and emerging ones Provides full and updated coverage of the areas of constrained Bayesian Optimization and Safe Bayesian Optimization Covers software resources, allowing readers to make informed and educated choices among the different platforms available to set up Bayesian Optimization components in academic and industrial activities Allows a full understanding of the basic algorithmic framework, including recent proposals about acquisition functions

Notă biografică

Francesco Archetti is Professor Emeritus of Computer Science and full Professor of Computer Science at the Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Italy. His research activities are focused on Data Analytics, Network Science, Probabilistic Modelling, Predictive Analytics, and Optimal Learning, with application to security, water management, logistics, and cyber-physical systems. He is one of the two authors of the Springer Brief Bayesian Optimization and Data Science (2019). Antonio Candelieri is an Associate Professor for the Department of Economics, Management, and Statistics at the University of Milano-Bicocca, Italy. His research activities are focused on Machine Learning and Bayesian Optimization. He was ranked within the "Top 2% Scientists, Stanford University Ranking 2023" and received a "Paper Award 2022 Honorable Mention" from the Journal of Global Optimization (Springer). Andrea Ponti is a PhD candidate at the Department of Economics, Management, and Statistics, University of Milano-Bicocca, Italy. His research focuses on the optimization of black-box functions using advanced Bayesian methods. From an industrial perspective, he designs and develops versatile machine learning solutions, focusing on foundation models and Large Language Models (LLMs, aka what's behind ChatGPT). Andrea Ponti is a PhD student in Data Science with a master’s degree in computer science. His research focuses on the optimization of complex black-box functions using advanced Bayesian methods. Alongside his academic work, he has practical experience developing machine learning solutions in industry, especially in the areas of foundation models and large language models. His work aims to connect research and real-world applications in a meaningful way.