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Bayesian Optimization and Data Science

  • Francesco Archetti
  • Antonio Candelieri

Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Francesco Archetti, Antonio Candelieri
    Pages 1-18
  3. Francesco Archetti, Antonio Candelieri
    Pages 19-35
  4. Francesco Archetti, Antonio Candelieri
    Pages 37-56
  5. Francesco Archetti, Antonio Candelieri
    Pages 57-72
  6. Francesco Archetti, Antonio Candelieri
    Pages 73-96
  7. Francesco Archetti, Antonio Candelieri
    Pages 97-109
  8. Francesco Archetti, Antonio Candelieri
    Pages 111-126

About this book

Introduction

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.

Keywords

Gaussian process acquisition functions knowledge gradient automatic algorithm configuration marketing MarkTech

Authors and affiliations

  • Francesco Archetti
    • 1
  • Antonio Candelieri
    • 2
  1. 1.Department of Computer Science, Systems and CommunicationsUniversity of Milano-BicoccaMilanItaly
  2. 2.Department of Computer Science, Systems and CommunicationsUniversity of Milano-BicoccaMilanItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-24494-1
  • Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-24493-4
  • Online ISBN 978-3-030-24494-1
  • Series Print ISSN 2190-8354
  • Series Online ISSN 2191-575X
  • Buy this book on publisher's site
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