本文针对局部机器遗忘问题，借鉴记忆化研究提出改进的参数局部化策略，并设计 DEL 算法重置并微调关键参数，在多项数据集和指标上取得最优结果。
摘要：
机器遗忘是指在机器学习模型完成训练后，高效地从模型中移除指定训练数据子集的影响。这对于关键应用十分重要，包括通过移除过时、错误标注或被投毒的数据来提升模型准确性。在本工作中，我们研究局部遗忘，即遗忘算法作用于一个已识别出的（较小）参数子集。受记忆化相关文献启发，我们提出一种改进的局部化策略，该策略与现有遗忘算法结合时能够产生优异结果。我们还提出一种新的遗忘算法 Deletion by Example Localization（DEL），它会重置根据我们的局部化策略被认为最关键的参数，然后对这些参数进行微调。我们在不同数据集、遗忘集和指标上开展的大量实验表明，DEL 在遗忘指标上树立了新的最优水平，对比对象既包括局部方法也包括全参数方法；同时，它只修改一小部分参数，并且在测试准确率方面也优于当前最优的局部遗忘方法。
英文摘要：
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this work, we study localized unlearning, where the unlearning algorithm operates on a (small) identified subset of parameters. Drawing inspiration from the memorization literature, we propose an improved localization strategy that yields strong results when paired with existing unlearning algorithms. We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them. Our extensive experiments on different datasets, forget sets and metrics reveal that DEL sets a new state-of-the-art for unlearning metrics, against both localized and full-parameter methods, while modifying a small subset of parameters, and outperforms the state-of-the-art localized unlearning in terms of test accuracy too.
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
Sydney AI Centre, The University of Sydney（悉尼人工智能中心，悉尼大学）；Hong Kong Baptist University（香港 Baptist 大学）；RIKEN AIP（日本理化学研究所AIP）；University of Technology Sydney（悉尼技术大学）
University of Chicago（芝加哥大学）；Argonne National Laboratory（阿贡国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Dartmouth College（达特茅斯学院）；International Computer Science Institute（国际计算机科学研究所）；University of California, Berkeley（加州大学伯克利分校）
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
Hong Kong Baptist University（香港浸会大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；The University of Sydney（悉尼大学）；The University of Tokyo（东京大学）
Inference-Time Machine Unlearning via Gated Activation Redirection
MALTA, Machine Learning Theory and Applications Lab, PUCRS, Porto Alegre, Brazil（MALTA机器学习理论与应用实验室，PUCRS，波士顿-阿尔格雷，巴西）；Harvard University（哈佛大学）；Kunumi Institute, Brazil（库努米研究所，巴西）
Unintended Memorization of Sensitive Information in Fine-Tuned Language Models
TUM University Hospital（慕尼黑工业大学医院）；Technical University of Munich (TUM)（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Computing, Imperial College London（伦敦帝国学院计算系）
ToFU: Transforming How Federated Learning Systems Forget User Data
School of Computer Science（计算机科学学院）；Statistics, Trinity College Dublin, Ireland（统计学，都柏林信任学院，爱尔兰）；School of Computer Science, University College Dublin, Ireland（计算机科学学院，都柏林大学学院，爱尔兰）
Beyond Forgetting: Representation Misdirection Elicits Controllable Side Behaviors and Capabilities
Japan Advanced Institute of Science and Technology（日本先进科学研究所）；Quantum AI and Cyber Security Institute（量子人工智能与网络安全研究所）；FPT Corporation（FPT公司）；Monash University（墨尔本大学）；RIKEN（理化学研究所）
Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Pittsburgh（匹兹堡大学）；University of Arizona（亚利桑那大学）；University of Central Florida（中佛罗里达大学）
Descend or Rewind? Stochastic Gradient Descent Unlearning
Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, United States（工程科学与应用数学系，西北大学，埃文斯顿，伊利诺伊州，美国）；Department of Industrial Engineering（工业工程系）；Applied Mathematics, Northwestern University, Evanston, IL, United States（应用数学，西北大学，埃文斯顿，伊利诺伊州，美国）
Towards Irreversible Machine Unlearning for Diffusion Models
Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系，设计与工程学院）；Betterdata, Singapore（新加坡Betterdata公司）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
