報告人：徐淼 博士 澳大利昆士蘭大學
報告時間：8月2日（周五）14:00
報告地點：九龍湖校區計算機樓106室
報告人簡介：Miao Xu is a Senior Lecturer in the School of Electrical Engineering and Computer Science at the University of Queensland, Australia. She was awarded the Australian Research Council Discovery Early Career Researcher Award (DECRA) in 2023. Dr. Xu specializes in machine learning, focusing on the challenges of learning from imperfect information. She earned her PhD from Nanjing University, where her research was recognized with the CAAI Outstanding Doctoral Dissertation Award and the IBM PhD Fellowship.
報告摘要：Machine unlearning aims to remove specific knowledge from a well-trained machine learning model. This topic has gained significant attention recently due to the widespread adoption of machine learning models across various applications and the accompanying privacy, legal, and ethical considerations. During the unlearning process, models are typically presented with data that specifies which information should be erased and which should be retained. Nonetheless, practical challenges arise due to prevalent issues of data quality issues and access restrictions. This paper explores these challenges and introduces strategies to address problems related to unsupervised data, weakly supervised data, and scenarios characterized by zero-shot and federated data availability. Finally, we discuss related open questions, particularly concerning evaluation metrics, how the forgetting information is represented and delivered, and the unique challenges posed by large generative models.
