Multiple Information Source Bayesian Optimization
暫譯: 多資訊來源貝葉斯優化
Candelieri, Antonio, Ponti, Andrea, Archetti, Francesco
- 出版商: Springer
- 出版日期: 2025-08-31
- 售價: $2,350
- 貴賓價: 9.5 折 $2,233
- 語言: 英文
- 頁數: 99
- 裝訂: Quality Paper - also called trade paper
- ISBN: 3031979648
- ISBN-13: 9783031979644
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
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.
商品描述(中文翻譯)
本書提供了多個資訊來源和多忠實度貝葉斯優化的全面回顧，特別專注於新穎的「增強高斯過程」（Augmented Gaussian Process）方法論。本書的重要性在於澄清使用多忠實度或多個資訊來源方法解決現實世界問題之間的關係和重要差異。根據特定問題的特徵選擇最合適的策略，確保最終解決方案的成功。本書還提供了可用軟體工具的概述：特別介紹了基於增強高斯過程的多資訊來源貝葉斯優化的兩個實作，其中一個是用 Python 實現的，並作為 BoTorch 的開發分支可用，最後還對其他可用的多忠實度和多資訊來源優化工具進行了比較分析，考慮了測試問題和現實世界應用。
本書將對兩個主要讀者群體有用：
1. 計算機科學、人工智慧、機器學習和優化的博士候選人
2. 希望在工程設計、材料科學和生物技術等領域實施有效且高效的實驗設計和優化計算成本高的實驗程序的學術界和業界研究人員。
作者簡介
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.
作者簡介(中文翻譯)
Francesco Archetti 是意大利米蘭比科卡大學（University of Milano-Bicocca）資訊、系統與通訊系（Department of Informatics, Systems and Communication, DISCo）的榮譽教授及全職教授。他的研究活動專注於數據分析（Data Analytics）、網絡科學（Network Science）、概率建模（Probabilistic Modelling）、預測分析（Predictive Analytics）和最佳學習（Optimal Learning），應用於安全性、水資源管理、物流和網絡物理系統（cyber-physical systems）。他是Springer Brief《貝葉斯優化與數據科學》（Bayesian Optimization and Data Science, 2019）的兩位作者之一。
Antonio Candelieri 是意大利米蘭比科卡大學經濟、管理與統計系（Department of Economics, Management, and Statistics）的副教授。他的研究活動專注於機器學習（Machine Learning）和貝葉斯優化（Bayesian Optimization）。他在「2023年史丹佛大學排名前2%科學家」中名列前茅，並獲得《全球優化期刊》（Journal of Global Optimization, Springer）2022年論文獎的榮譽提名。Andrea Ponti 是意大利米蘭比科卡大學經濟、管理與統計系的博士候選人。他的研究專注於使用先進的貝葉斯方法優化黑箱函數（black-box functions）。從產業的角度來看，他設計並開發多功能的機器學習解決方案，專注於基礎模型（foundation models）和大型語言模型（Large Language Models, LLMs，亦即ChatGPT背後的技術）。
Andrea Ponti 是一名數據科學（Data Science）博士生，擁有計算機科學碩士學位。他的研究專注於使用先進的貝葉斯方法優化複雜的黑箱函數。除了學術工作外，他在產業中開發機器學習解決方案方面也有實際經驗，特別是在基礎模型和大型語言模型的領域。他的工作旨在以有意義的方式將研究與現實世界應用相連接。
