Trustworthy Machine Learning Under Imperfect Data
暫譯: 不完美數據下的可信機器學習

Han, Bo, Liu, Tongliang

Trustworthy Machine Learning Under Imperfect Data
  • 出版商: Springer
  • 出版日期: 2025-10-20
  • 售價: $7,200
  • 貴賓價: 9.5 折 $6,840
  • 語言: 英文
  • 頁數: 292
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819693950
  • ISBN-13: 9789819693955
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

The subject of this book centres around trustworthy machine learning under imperfect data. It is primarily designed for scientists, researchers, practitioners, professionals, postgraduates and undergraduates in the field of machine learning and artificial intelligence. The book focuses on trustworthy deep learning under various types of imperfect data, including noisy labels, adversarial examples, and out-of-distribution data. It covers trustworthy machine learning algorithms, theories, and systems.

The main goal of the book is to provide students and researchers in academia with an unbiased and comprehensive literature review. More importantly, it aims to stimulate insightful discussions about the future of trustworthy machine learning. By engaging the audience in more in-depth conversations, the book intends to spark ideas for addressing core problems in this topic. For example, it will explore how to build up benchmark datasets in noisy-supervised learning, how to tackle the emerging adversarial learning, and how to tackle out-of-distribution detection.

For practitioners in the industry, this book will present state-of-the-art trustworthy machine learning methods to help them solve real-world problems in different scenarios, such as online recommendation and web search. While the book will introduce the basics of knowledge required, readers will benefit from having some familiarity with linear algebra, probability, machine learning, and artificial intelligence. The emphasis will be on conveying the intuition behind all formal concepts, theories, and methodologies, ensuring the book remains self-contained at a high level.

商品描述(中文翻譯)

本書的主題圍繞在不完美數據下的可信機器學習。它主要為機器學習和人工智慧領域的科學家、研究人員、從業者、專業人士、研究生和本科生而設計。該書專注於在各種不完美數據類型下的可信深度學習，包括噪聲標籤、對抗樣本和分佈外數據。內容涵蓋可信機器學習的算法、理論和系統。

本書的主要目標是為學術界的學生和研究人員提供一個公正且全面的文獻回顧。更重要的是，它旨在激發對可信機器學習未來的深入討論。通過引導讀者進行更深入的對話，本書希望激發解決該主題核心問題的想法。例如，它將探討如何在噪聲監督學習中建立基準數據集、如何應對新興的對抗學習，以及如何處理分佈外檢測。

對於業界的從業者，本書將介紹最先進的可信機器學習方法，以幫助他們在不同場景中解決現實世界的問題，例如在線推薦和網頁搜索。雖然本書將介紹所需的基本知識，但讀者若對線性代數、概率、機器學習和人工智慧有一定的熟悉度將會更有幫助。重點將放在傳達所有正式概念、理論和方法背後的直覺，確保本書在高層次上保持自足。

作者簡介

Prof. Bo Han is an Assistant Professor in Machine Learning at Hong Kong Baptist University and a BAIHO Visiting Scientist at RIKEN AIP, where his research focuses on machine learning, deep learning, foundation models and their applications. He was a Visiting Faculty Researcher at Microsoft Research and a Postdoc Fellow at RIKEN AIP. He has co authored a machine learning monograph by MIT Press. He has served as Area Chairs of NeurIPS, ICML, ICLR and UAI. He has also served as Action Editors and Editorial Board Members of JMLR, MLJ, JAIR, TMLR and IEEE TNNLS. He received the Outstanding Paper Award at NeurIPS and Outstanding Area Chair at ICLR. He received the RIKEN BAIHO Award (2019), RGC Early CAREER Scheme (2020), Microsoft Research StarTrack Program (2021), and Tencent AI Faculty Research Award (2022).

Prof. Tongliang Liu is the Director of Sydney AI Centre at University of Sydney, Australia; a Visiting Professor of University of Science and Technology of China, Hefei, China; a Visiting Scientist of RIKEN AIP, Tokyo, Japan; and a Visiting Associate Professor at Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates. He has published more than 100 papers at leading ML/AI conferences and journals. He is regularly the meta reviewer of ICML, NeurIPS, ICLR, UAI, IJCAI, and AAAI. He is the Action Editor of Transactions on Machine Learning Research, Associate Editor of ACM Computing Surveys, and in the Editorial Board of Journal of Machine Learning Research and the Machine Learning journal. He received the ARC DECRA Award in 2018, ARC Future Fellowship Award in 2022, and IEEE AI's 10 to Watch Award in 2023. He also received multiple faculty awards, e.g., from OPPO and Meituan.

作者簡介(中文翻譯)

韓博教授是香港浸會大學的機器學習助理教授，也是RIKEN AIP的BAIHO訪問科學家，他的研究專注於機器學習、深度學習、基礎模型及其應用。他曾擔任微軟研究院的訪問教職研究員，以及RIKEN AIP的博士後研究員。他與MIT Press共同撰寫了一本機器學習專著。他曾擔任NeurIPS、ICML、ICLR和UAI的區域主席，並擔任JMLR、MLJ、JAIR、TMLR和IEEE TNNLS的行動編輯和編輯委員會成員。他在NeurIPS獲得了優秀論文獎，並在ICLR獲得了優秀區域主席獎。他獲得了RIKEN BAIHO獎（2019年）、RGC早期職業計劃（2020年）、微軟研究星際計劃（2021年）和騰訊AI教職研究獎（2022年）。

劉同亮教授是澳大利亞悉尼大學悉尼AI中心的主任；中國科學技術大學的訪問教授；日本東京RIKEN AIP的訪問科學家；以及阿布達比穆罕默德·本·扎耶德人工智慧大學的訪問副教授。他在領先的機器學習/人工智慧會議和期刊上發表了超過100篇論文。他定期擔任ICML、NeurIPS、ICLR、UAI、IJCAI和AAAI的元審稿人。他是《機器學習研究通訊》的行動編輯、《ACM計算調查》的副編輯，並在《機器學習研究期刊》和《機器學習期刊》的編輯委員會中任職。他在2018年獲得ARC DECRA獎，2022年獲得ARC未來獎學金，並在2023年獲得IEEE AI的10位值得關注獎。他還獲得了多個教職獎項，例如來自OPPO和美團的獎項。

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