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.
"Chapter1-Introduction".- "Chapter-2,Trustworthy Machine Learning with
Noisy Labels".- "Chapter-3,Trustworthy Machine Learning with Adversarial
Examples".- "Chapter-4,Trustworthy Machine Learning with Out-of-distribution
Data".- "Chapter-5,Advance Topics in Trustworthy Machine Learning".
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.
