Bayesian Optimization and Data Science (SpringerBriefs in Optimization) - Softcover

Über die Autorin bzw. den Autor

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.

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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.