Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models. While approximating a straightforward yet expensive approach of retrain-from-scratch, recent machine unlearning methods unlearn a sample by updating weights to remove its influence on the weight parameters. In this paper, we introduce a simple yet effective approach to remove a data influence on the deep generative model. Inspired by works in multi-task learning, we propose to manipulate gradients to regularize the interplay of influence among samples by projecting gradients onto the normal plane of the gradients to be retained. Our work is agnostic to statistics of the removal samples, outperforming existing baselines while providing theoretical analysis for the first time in unlearning a generative model.
Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
Institute of Artificial Intelligence, Beihang University（北航人工智能研究院）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University（北京未来区块链与隐私计算先进创新中心）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Amsterdam（阿姆斯特丹大学）；Tsinghua University（清华大学）；YanTron Technology Co.Ltd（燕tron技术有限公司）
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
School of Information Engineering, Zhongnan University of Economics and Law（信息工程学院，中南财经政法大学）；China University of Geosciences, Wuhan（中国地质大学（武汉））；City University of Macau（澳门城市大学）
Machine Unlearning in Low-Dimensional Feature Subspace
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China（电子工程系，香港理工大学，香港，中国）；School of Automation, Beijing Institute of Technology, Beijing, China（自动化学院，北京理工大学，北京，中国）
Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?
NewraLab；Beihang University（北京航空航天大学）；GoerTek Alpha Lab（歌尔Alpha实验室）；Skyline University Nigeria（尼日利亚天际线大学）；UESTC（电子科技大学）；Imperial College London（帝国理工学院）
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Southeast University（东南大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of California, Berkeley（加州大学伯克利分校）；Nanyang Technological University（南洋理工大学）
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
Michigan State University（密歇根州立大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；MIT-IBM Watson AI Lab, IBM Research（麻省理工学院-IBM沃森人工智能实验室，IBM研究院）
Who Benefits From Sinus Surgery? Comparing Generative AI and Supervised Machine Learning for Predicting Surgical Outcomes in Chronic Rhinosinusitis
Department of Computer Science, Purdue University（计算机科学系，普渡大学）；Department of Computer Science, Indiana University Indianapolis（计算机科学系，印第安纳大学印第安纳波利斯分校）；Department of Otolaryngology—Head and Neck Surgery, Indiana University School of Medicine（耳鼻喉科—头颈外科，印第安纳大学医学院）
