
Machine Unlearning: Concepts and Implementations
Description
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Persons
Dr. Weiqi Wang is a postdoc at University of Technology Sydney. He received his PhD degree with the School of Computer Science, University of Technology Sydney, advised by Professor Shui Yu. He previously worked as a senior algorithm engineer at the Department of AI-Strategy, Local Consumer Services Segment, Alibaba Group. He has been actively involved in the research community by serving as a reviewer for prestige journals such as ACM Computing Surveys, IEEE Communications Surveys and Tutorials, IEEE TIFS, IEEE TDSC, IEEE TIP, IEEE TMC, IEEE Transactions on SMC, and IEEE IOTJ, and international conferences such as ICML, CVPR, WWW, ICLR, IEEE ICC, and IEEE GLOBECOM. His research interests focus on security and privacy for trustworhty AI.
Prof. Shui Yu is currently a Professor of School of Computer Science, Deputy Chair of University Research Committee, University of Technology Sydney, Australia. His research interest includes Mathematical AI, Cybersecurity, Network Science, and Big Data. He has published seven monographs and edited two books, more than 650 technical papers at different venues. His current h-index is 86. Professor Yu promoted the research field of networking for big data since 2013, and his research outputs have been widely adopted by industrial systems, such as Amazon cloud security. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials (Area Editor), IEEE Transactions on Cognitive Communications and Networking, and IEEE Transactions on Dependable and Secure Computing. He is a Distinguished Visitor of IEEE Computer Society, and an elected member of the Board of Governors of IEEE Communications Society. He is a member of ACM and AAAS, and a Fellow of IEEE.
Content
.- Chapter 1: Introduction of Machine Unlearning.- Chapter 2: Exact Machine Unlearning.- Chapter 3: Approximate Machine Unlearning.- Chapter 4: Auditing Machine Unlearning.- Chapter 5: Graph Unlearning.- Chapter 6: Federated Unlearning.- Chapter 7: Machine Unlearning in Large Language Models (LLMs).- Chapter 8 Machine Unlearning in Diffusion Models.-Chapter 9 Privacy and Security in Machine Unlearning.- Chapter 10 The Future Work.