LEADER 03530nam 22005415 450 001 9911046543603321 005 20251020130402.0 010 $a981-9693-96-9 024 7 $a10.1007/978-981-96-9396-2 035 $a(MiAaPQ)EBC32364754 035 $a(Au-PeEL)EBL32364754 035 $a(CKB)41689375000041 035 $a(DE-He213)978-981-96-9396-2 035 $a(OCoLC)1546776599 035 $a(EXLCZ)9941689375000041 100 $a20251020d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTrustworthy Machine Learning under Imperfect Data /$fby Bo Han, Tongliang Liu 205 $a1st ed. 2026. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2026. 215 $a1 online resource (581 pages) 225 1 $aComputer Science Series 311 08$a981-9693-95-0 327 $a"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". 330 $aThe subject of this book centres around trustworthy machine learning under imperfect data. It is primarily designed for scientists, researchers, practitioners, professionals, postgraduates and undergraduates in the field of machine learning and artificial intelligence. The book focuses on trustworthy deep learning under various types of imperfect data, including noisy labels, adversarial examples, and out-of-distribution data. It covers trustworthy machine learning algorithms, theories, and systems. The main goal of the book is to provide students and researchers in academia with an unbiased and comprehensive literature review. More importantly, it aims to stimulate insightful discussions about the future of trustworthy machine learning. By engaging the audience in more in-depth conversations, the book intends to spark ideas for addressing core problems in this topic. For example, it will explore how to build up benchmark datasets in noisy-supervised learning, how to tackle the emerging adversarial learning, and how to tackle out-of-distribution detection. For practitioners in the industry, this book will present state-of-the-art trustworthy machine learning methods to help them solve real-world problems in different scenarios, such as online recommendation and web search. While the book will introduce the basics of knowledge required, readers will benefit from having some familiarity with linear algebra, probability, machine learning, and artificial intelligence. The emphasis will be on conveying the intuition behind all formal concepts, theories, and methodologies, ensuring the book remains self-contained at a high level. . 410 0$aComputer Science Series 606 $aMachine learning 606 $aArtificial intelligence 606 $aMachine Learning 606 $aArtificial Intelligence 615 0$aMachine learning. 615 0$aArtificial intelligence. 615 14$aMachine Learning. 615 24$aArtificial Intelligence. 676 $a006.31 700 $aHan$b Bo$01436779 701 $aLiu$b Tongliang$01448665 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911046543603321 996 $aTrustworthy Machine Learning under Imperfect Data$94468768 997 $aUNINA