Trustworthy Machine Learning under Imperfect Data

Author:   Bo Han ,  Tongliang Liu
Publisher:   Springer Nature Switzerland AG
ISBN:  

9789819693955


Pages:   292
Publication Date:   20 October 2025
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Trustworthy Machine Learning under Imperfect Data


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Author:   Bo Han ,  Tongliang Liu
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9789819693955


ISBN 10:   9819693950
Pages:   292
Publication Date:   20 October 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Author Information

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.

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