Bayesian Optimization and Data Science

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. Front Matter ....Pages i-xiii Automated Machine Learning and Bayesian Optimization (Francesco Archetti, Antonio Candelieri)....Pages 1-18 From Global Optimization to Optimal Learning (Francesco Archetti, Antonio Candelieri)....Pages 19-35 The Surrogate Model (Francesco Archetti, Antonio Candelieri)....Pages 37-56 The Acquisition Function (Francesco Archetti, Antonio Candelieri)....Pages 57-72 Exotic Bayesian Optimization (Francesco Archetti, Antonio Candelieri)....Pages 73-96 Software Resources (Francesco Archetti, Antonio Candelieri)....Pages 97-109 Selected Applications (Francesco Archetti, Antonio Candelieri)....Pages 111-126