This GitHub repository contains an updated list of Federated Learning papers as of October 29, 2024.
- The resources are collected from various sources, including arXiv, NeurIPS, ICML, ICLR, ACL, EMNLP, AAAI, IJCAI, KDD, CVPR, ICCV, ECCV, NIPS, IEEE, ACM, Springer, ScienceDirect, Wiley, Nature, Science, and other top AI/ML conferences and journals.
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Explore additional research papers on the following topics:
- For Large Language Models papers, please visit the LLM Repository.
- For Backdoor Learning papers, please visit the Backdoor Learning Repository.
- For Federated Learning papers, please visit the Federated Learning Repository.
- For Machine Unlearning papers, please visit the Machine Unlearning Repository.
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Due to GitHub repository limitations, this section includes only those papers that provide accompanying code, sorted by publish date. For access to the full list of papers, please visit the Shinyapps website.
| No. | Title | Authors | Publish Date | Venue | Code | URL |
|---|---|---|---|---|---|---|
| 1 | Evaluating Deep Unlearning in Large Language Models | Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, Kamalika Chaudhuri | 2024-10-19 | arXiv …, 2024 | https://github.com/wrh14/deep_unlearning | http://arxiv.org/abs/2410.15153v1 |
| 2 | Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts | Hongcheng Gao, Tianyu Pang, Chao Du, Taihang Hu, Zhijie Deng, Min Lin | 2024-10-17 | arXiv …, 2024 | https://github.com/sail-sg/Meta-Unlearning | http://arxiv.org/abs/2410.12777v1 |
| 3 | Efficient Federated Unlearning under Plausible Deniability | Ayush K. Varshney, Vicenç Torra | 2024-10-13 | arXiv:2410.09947, 2024 | https://github.com/Ayush-Umu/Federated-Unlearning-under-Plausible-Deniability | http://arxiv.org/abs/2410.09947v1 |
| 4 | A Closer Look at Machine Unlearning for Large Language Models | Xiaojian Yuan, Tianyu Pang, Chao Du, Kejiang Chen, Weiming Zhang, Min Lin | 2024-10-10 | arXiv | https://github.com/sail-sg/closer-look-LLM-unlearning | http://arxiv.org/abs/2410.08109v1 |
| 5 | Dissecting Fine-Tuning Unlearning in Large Language Models | Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, Haiqin Yang | 2024-10-09 | arXiv | https://github.com/yihuaihong/Dissecting-FT-Unlearning | http://arxiv.org/abs/2410.06606v1 |
| 6 | Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning | Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu | 2024-10-09 | arXiv | https://github.com/OPTML-Group/Unlearn-Simple | http://arxiv.org/abs/2410.07163v1 |
| 7 | A Probabilistic Perspective on Unlearning and Alignment for Large Language Models | Yan Scholten, Stephan Günnemann, Leo Schwinn | 2024-10-04 | arXiv:2410.03523, 2024 | https://github.com/yascho/probabilistic-unlearning | http://arxiv.org/abs/2410.03523v1 |
| 8 | Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need | Xianlong Wang, Minghui Li, Wei Liu, Hangtao Zhang, Shengshan Hu, Yechao Zhang, Ziqi Zhou, Hai Jin | 2024-10-04 | arXiv | https://github.com/CGCL-codes/UnlearnablePC | http://arxiv.org/abs/2410.03644v1 |
| 9 | Multimodal Unlearnable Examples: Protecting Data against Multimodal Contrastive Learning | Xinwei Liu, Xiaojun Jia, Yuan Xun, Siyuan Liang, Xiaochun Cao | 2024-10 | MM '24: Proceedings of the 32nd ACM International Conference on Multimedia | https://github.com/thinwayliu/Multimodal-Unlearnable-Examples | https://dl.acm.org/doi/10.1145/3664647.3680708 |
| 10 | DuplexGuard: Safeguarding Deletion Right in Machine Unlearning Via Duplex Watermarking | X. Zhang, C. Zhang, J. Lou, K. Wu, Z. Wang, X. Chen | 2024-09-11 | IEEE Transactions on Dependable and Secure Computing | https://github.com/123000001212/DuplexGuard | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10670570 |
| 11 | Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models | Aakash Sen Sharma, Niladri Sarkar, Vikram S. Chundawat, Ankur A. Mali, Murari Mandal | 2024-09-10 | arXiv | https://respailab.github.io/unlearning-or-concealment | https://doi.org/10.48550/arXiv.2409.05668 |
| 12 | CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence | Chaochao Chen, Jiaming Zhang, Yizhao Zhang, Li Zhang, Lingjuan Lyu, Yuyuan Li, Biao Gong, Chenggang Yan | 2024-08-27 | arXiv | https://github.com/xiye7lai/CURE4Rec | https://doi.org/10.48550/arXiv.2408.14393 |
| 13 | Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier | Lu Yi, Zhewei Wei | 2024-08-17 | arXiv | https://github.com/luyi256/ScaleGUN | http://arxiv.org/abs/2408.09212v2 |
| 14 | Machine Unlearning in Generative AI: A Survey | Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, Meng Jiang | 2024-07-31 | arXiv | https://github.com/franciscoliu/GenAI-MU-Reading | https://doi.org/10.48550/arXiv.2407.20516 |
| 15 | Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective | Yujian Liu, Yang Zhang, Tommi S. Jaakkola, Shiyu Chang | 2024-07-25 | arXiv | https://github.com/UCSB-NLP-Chang/causal_unlearn | https://doi.org/10.48550/arXiv.2407.16997 |
| 16 | Safe Unlearning: A Surprisingly Effective and Generalizable Solution to Defend Against Jailbreak Attacks | Zhexin Zhang, Junxiao Yang, Pei Ke, Shiyao Cui, Chujie Zheng, Hongning Wang, Minlie Huang | 2024-07-04 | arXiv | https://github.com/thu-coai/SafeUnlearning | https://doi.org/10.48550/arXiv.2407.02855 |
| 17 | Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging | Zhipeng Deng, Luyang Luo, Hao Chen | 2024-07-03 | arXiv | https://github.com/dzp2095/FCU | https://doi.org/10.48550/arXiv.2407.02356 |
| 18 | To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models | Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng, Qingbin Liu, Mengru Wang, Dianbo Sui, Xi Chen, Huajun Chen, Ningyu Zhang | 2024-07-03 | arXiv | https://github.com/zjunlp/KnowUnDo | https://doi.org/10.48550/arXiv.2407.01920 |
| 19 | Intrinsic Evaluation of Unlearning Using Parametric Knowledge Traces | Yihuai Hong, Lei Yu, Shauli Ravfogel, Haiqin Yang, Mor Geva | 2024-06-18 | arXiv | https://github.com/yihuaihong/ConceptVectors | https://doi.org/10.48550/arXiv.2406.11614 |
| 20 | Soft Prompting for Unlearning in Large Language Models | Karuna Bhaila, Minh-Hao Van, Xintao Wu | 2024-06-17 | arXiv | https://github.com/karuna-bhaila/llm_unlearning | https://doi.org/10.48550/arXiv.2406.12038 |
| 21 | RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models | Zhuoran Jin, Pengfei Cao, Chenhao Wang, Zhitao He, Hongbang Yuan, Jiachun Li, Yubo Chen, Kang Liu, Jun Zhao | 2024-06-16 | arXiv | http://rwku-bench.github.io | https://doi.org/10.48550/arXiv.2406.10890 |
| 22 | Data Attribution for Text-to-Image Models by Unlearning Synthesized Images | Sheng-Yu Wang, Aaron Hertzmann, Alexei A. Efros, Jun-Yan Zhu, Richard Zhang | 2024-06-14 | arXiv | https://peterwang512.github.io/AttributeByUnlearning | https://doi.org/10.48550/arXiv.2406.09408 |
| 23 | REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space | Tomer Ashuach, Martin Tutek, Yonatan Belinkov | 2024-06-13 | arXiv | https://technion-cs-nlp.github.io/REVS | https://doi.org/10.48550/arXiv.2406.09325 |
| 24 | Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference | Jiabao Ji, Yujian Liu, Yang Zhang, Gaowen Liu, Ramana Rao Kompella, Sijia Liu, Shiyu Chang | 2024-06-12 | arXiv | https://github.com/UCSB-NLP-Chang/ULD | https://doi.org/10.48550/arXiv.2406.08607 |
| 25 | Towards Efficient Machine Unlearning with Data Augmentation: Guided Loss-Increasing (GLI) to Prevent the Catastrophic Model Utility Drop | Dasol Choi, Soora Choi, Eunsun Lee, Jinwoo Seo, Dongbin Na | 2024-06-12 | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | https://github.com/Dasol-Choi/Guided_Loss_Increasing | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10678506 |
| 26 | Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience | Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen, Thanh Tam Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer | 2024-05-29 | arXiv | https://github.com/thanhtrunghuynh93/fastFedUL | https://doi.org/10.48550/arXiv.2405.18040 |
| 27 | A Study Regarding Machine Unlearning on Facial Attribute Data | Emircan Gündogdu, Altay Unal, Gozde Unal | 2024-05-27 | 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG) | https://github.com/ituvisionlab/face-attribute-unlearning | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10581972 |
| 28 | Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models | Yimeng Zhang, Xin Chen, Jinghan Jia, Yihua Zhang, Chongyu Fan, Jiancheng Liu, Mingyi Hong, Ke Ding, Sijia Liu | 2024-05-24 | arXiv | https://github.com/OPTML-Group/AdvUnlearn | https://doi.org/10.48550/arXiv.2405.15234 |
| 29 | Generative Unlearning for Any Identity | Juwon Seo, Sung-Hoon Lee, Tae-Young Lee, Seungjun Moon, Gyeong-Moon Park | 2024-05-17 | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | https://github.com/KHU-AGI/GUIDE | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10655907 |
| 30 | Erasing Concepts from Text-to-Image Diffusion Models with Few-shot Unlearning | Masane Fuchi, Tomohiro Takagi | 2024-05-12 | arXiv | https://github.com/fmp453/few-shot-erasing | https://doi.org/10.48550/arXiv.2405.07288 |
| 31 | Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders | Yi Yu, Yufei Wang, Song Xia, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex C. Kot | 2024-05-02 | arXiv | https://github.com/yuyi-sd/D-VAE | https://doi.org/10.48550/arXiv.2405.01460 |
| 32 | Breaking the Trilemma of Privacy, Utility, and Efficiency via Controllable Machine Unlearning | Zheyuan Liu, Guangyao Dou, Eli Chien, Chunhui Zhang, Yijun Tian, Ziwei Zhu | 2024-05 | WWW '24: Proceedings of the ACM on Web Conference 2024 | https://github.com/guangyaodou/ConMU | https://dl.acm.org/doi/10.1145/3589334.3645669 |
| 33 | Machine Unlearning for Document Classification | Lei Kang, Mohamed Ali Souibgui, Fei Yang, Lluís Gómez, Ernest Valveny, Dimosthenis Karatzas | 2024-04-29 | arXiv | https://github.com/leitro/MachineUnlearning-DocClassification | https://doi.org/10.48550/arXiv.2404.19031 |
| 34 | SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning | Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal, James Diffenderfer, Bhavya Kailkhura, Sijia Liu | 2024-04-28 | arXiv | https://github.com/OPTML-Group/SOUL | https://doi.org/10.48550/arXiv.2404.18239 |
| 35 | Machine Unlearning via Null Space Calibration | Huiqiang Chen, Tianqing Zhu, Xin Yu, Wanlei Zhou | 2024-04-21 | arXiv | https://github.com/HQC-ML/Machine-Unlearning-via-Null-Space-Calibration | https://doi.org/10.48550/arXiv.2404.13588 |
| 36 | LMEraser: Large Model Unlearning through Adaptive Prompt Tuning | Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia | 2024-04-18 | arXiv | https://github.com/lmeraser/lmeraser | https://doi.org/10.48550/arXiv.2404.11056 |
| 37 | Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge | Weikai Lu, Ziqian Zeng, Jianwei Wang, Zhengdong Lu, Zelin Chen, Huiping Zhuang, Cen Chen | 2024-04-08 | arXiv | https://github.com/ZeroNLP/Eraser | https://doi.org/10.48550/arXiv.2404.05880 |
| 38 | Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning | Chongyu Fan, Jiancheng Liu, Alfred O. Hero III, Sijia Liu | 2024-03-13 | arXiv | https://github.com/OPTML-Group/Unlearn-WorstCase | https://doi.org/10.48550/arXiv.2403.07362 |
| 39 | Towards Efficient and Effective Unlearning of Large Language Models for Recommendation | Hangyu Wang, Jianghao Lin, Bo Chen, Yang Yang, Ruiming Tang, Weinan Zhang, Yong Yu | 2024-03-07 | arXiv | https://github.com/justarter/E2URec | https://doi.org/10.48550/arXiv.2403.03536 |
| 40 | Dissecting Language Models: Machine Unlearning via Selective Pruning | Nicholas Pochinkov, Nandi Schoots | 2024-03-02 | arXiv | https://github.com/nickypro/selective-pruning | https://doi.org/10.48550/arXiv.2403.01267 |
| 41 | Machine Unlearning of Pre-trained Large Language Models | Jin Yao, Eli Chien, Minxin Du, Xinyao Niu, Tianhao Wang, Zezhou Cheng, Xiang Yue | 2024-02-23 | arXiv | https://github.com/yaojin17/Unlearning_LLM | https://doi.org/10.48550/arXiv.2402.15159 |
| 42 | Corrective Machine Unlearning | Shashwat Goel, Ameya Prabhu, Philip H. S. Torr, Ponnurangam Kumaraguru, Amartya Sanyal | 2024-02-22 | arXiv | https://github.com/drimpossible/corrective-unlearning-bench | https://doi.org/10.48550/arXiv.2402.14015 |
| 43 | Machine Unlearning for Image-to-Image Generative Models | Guihong Li, Hsiang Hsu, Chun-Fu Chen, Radu Marculescu | 2024-02-01 | arXiv | https://github.com/jpmorganchase/l2l-generator-unlearning | https://doi.org/10.48550/arXiv.2402.00351 |
| 44 | Game-Theoretic Unlearnable Example Generator | Shuang Liu, Yihan Wang, Xiao-Shan Gao | 2024-01-31 | AAAI | https://github.com/hong-xian/gue | https://doi.org/10.1609/aaai.v38i19.30130 |
| 45 | Dataset Condensation Driven Machine Unlearning | Junaid Iqbal Khan | 2024-01-31 | arXiv | https://github.com/algebraicdianuj/DC_U | https://doi.org/10.48550/arXiv.2402.00195 |
| 46 | TOFU: A Task of Fictitious Unlearning for LLMs | Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, J. Zico Kolter | 2024-01-12 | arXiv | https://locuslab.github.io/tofu/ | https://doi.org/10.48550/arXiv.2401.06121 |
| 47 | A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion Models | Rui Ma, Qiang Zhou, Yizhu Jin, Daquan Zhou, Bangjun Xiao, Xiuyu Li, Yi Qu, Aishani Singh, Kurt Keutzer, Jingtong Hu, Xiaodong Xie, Zhen Dong, Shanghang Zhang, Shiji Zhou | 2024-01-04 | arXiv | https://rmpku.github.io/CPDM-page/ | http://arxiv.org/abs/2403.12052v3 |
| 48 | Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection | Tuan Hoang, Santu Rana, Sunil Gupta, Svetha Venkatesh | 2024-01-01 | 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) | https://github.com/hnanhtuan/projected_gradient_unlearning | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10483632 |
| 49 | Machine Unlearning via Representation Forgetting With Parameter Self-Sharing | Weiqi Wang, Chenhan Zhang, Zhiyi Tian, Shui Yu | 2024 | IEEE Transactions on Information Forensics and Security | https://github.com/wwq5-code/RFU-SS | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10312776 |
| 50 | To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now | Yimeng Zhang, Jinghan Jia, Xin Chen, Aochuan Chen, Yihua Zhang, Jiancheng Liu, Ke Ding, Sijia Liu | 2024 | arXiv | https://github.com/OPTML-Group/Diffusion-MU-Attack | https://doi.org/10.48550/arXiv.2310.11868 |
| 51 | Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise | Yixin Liu, Kaidi Xu, Xun Chen, Lichao Sun | 2024 | AAAI | https://github.com/liuyixin-louis/Stable-Unlearnable-Example | https://doi.org/10.1609/aaai.v38i4.28169 |
| 52 | SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation | Chongyu Fan, Jiancheng Liu, Yihua Zhang, Dennis Wei, Eric Wong, Sijia Liu | 2024 | arXiv | https://github.com/OPTML-Group/Unlearn-Saliency | https://doi.org/10.48550/arXiv.2310.12508 |
| 53 | Task-Aware Machine Unlearning and Its Application in Load Forecasting | Wangkun Xu, Fei Teng | 2024 | IEEE Transactions on Power Systems | https://github.com/xuwkk/task_aware_machine_unlearning | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10472091 |
| 54 | Fast Yet Effective Machine Unlearning | Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, Mohan S. Kankanhalli | 2024 | IEEE Transactions on Neural Networks and Learning Systems | https://github.com/vikram2000b/Fast-Machine-Unlearning | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10113700 |
| 55 | Fair Machine Unlearning: Data Removal while Mitigating Disparities | Alex Oesterling, Jiaqi Ma, Flávio P. Calmon, Himabindu Lakkaraju | 2024 | arXiv | https://github.com/AI4LIFE-GROUP/fair-unlearning | https://doi.org/10.48550/arXiv.2307.14754 |
| 56 | Continual Forgetting for Pre-Trained Vision Models | H. Zhao, B. Ni, J. Fan, Y. Wang, Y. Chen, G. Meng, Z. Zhang | 2024 | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | https://github.com/bjzhb666/GS-LoRA | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10655987 |
| 57 | BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning | S. Liang, M. Zhu, A. Liu, B. Wu, X. Cao, E. -C. Chang | 2024 | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | https://github.com/LiangSiyuan21/BadCLIP | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10658003 |
| 58 | FedCSA: Boosting the Convergence Speed of Federated Unlearning under Data Heterogeneity | Zhen Wang, Daniyal M. Alghazzawi, Li Cheng, Gaoyang Liu, Chen Wang, Zeng Cheng, Yang Yang | 2023-12-21 | 2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) | https://github.com/ZhenWang9/FedCSA | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10491845 |
| 59 | Deep Unlearning: Fast and Efficient Training-free Approach to Controlled Forgetting | Sangamesh Kodge, Gobinda Saha, Kaushik Roy | 2023-12-01 | arXiv | https://github.com/sangamesh-kodge/class_forgetting | https://doi.org/10.48550/arXiv.2312.00761 |
| 60 | Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems | Dasol Choi, Dongbin Na | 2023-11-03 | arXiv | https://github.com/ndb796/MachineUnlearning | https://doi.org/10.48550/arXiv.2311.02240 |
| 61 | A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions | Yang Zhao, Jiaxi Yang, Yiling Tao, Lixu Wang, Xiaoxiao Li, Dusit Niyato | 2023-10-30 | arXiv | https://github.com/abbottyanginchina/Awesome-Federated-Unlearning | https://doi.org/10.48550/arXiv.2310.19218 |
| 62 | Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples | Wan Jiang, Yunfeng Diao, He Wang, Jianxin Sun, Meng Wang, Richang Hong | 2023-10 | MM '23: Proceedings of the 31st ACM International Conference on Multimedia | https://github.com/jiangw-0/LE_JCDP | https://dl.acm.org/doi/10.1145/3581783.3611833 |
| 63 | What Can We Learn from Unlearnable Datasets? | Pedro Sandoval Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein | 2023-05-30 | NeurIPS | https://github.com/psandovalsegura/learn-from-unlearnable | http://papers.nips.cc/paper_files/paper/2023/hash/ee5bb72130c332c3d4bf8d231e617506-Abstract-Conference.html |
| 64 | Model Sparsification Can Simplify Machine Unlearning | Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu | 2023-04-11 | arXiv | https://github.com/OPTML-Group/Unlearn-Sparse | https://doi.org/10.48550/arXiv.2304.04934 |
| 65 | GIF: A General Graph Unlearning Strategy via Influence Function | Jiancan Wu, Yi Yang, Yuchun Qian, Yongduo Sui, Xiang Wang, Xiangnan He | 2023-04-06 | WWW '23: Proceedings of the ACM Web Conference 2023 | https://github.com/wujcan/GIF-torch/ | https://dl.acm.org/doi/10.1145/3543507.3583521 |
| 66 | Inductive Graph Unlearning | Cheng-Long Wang, Mengdi Huai, Di Wang | 2023-04-06 | USENIX Security Symposium | https://github.com/Happy2Git/GUIDE | https://www.usenix.org/conference/usenixsecurity23/presentation/wang-cheng-long |
| 67 | Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks | Tianrui Qin, Xitong Gao, Juanjuan Zhao, Kejiang Ye, Cheng-Zhong Xu | 2023-03-27 | arXiv | https://github.com/lafeat/ueraser | https://doi.org/10.48550/arXiv.2303.15127 |
| 68 | Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks Using an Incompetent Teacher | Vikram S. Chundawat, Ayush K. Tarun, Murari Mandal, Mohan S. Kankanhalli | 2023 | AAAI | https://github.com/vikram2000b/bad-teaching-unlearning | https://doi.org/10.1609/aaai.v37i6.25879 |
| 69 | ModelGiF: Gradient Fields for Model Functional Distance | J. Song, Z. Xu, S. Wu, G. Chen, M. Song | 2023 | 2023 IEEE/CVF International Conference on Computer Vision (ICCV) | https://github.com/zju-vipa/modelgif | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10377295 |
| 70 | Zero-Shot Machine Unlearning | Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mohan Kankanhalli | 2023 | IEEE Transactions on Information Forensics and Security | https://github.com/ayu987/zero-shot-unlearning | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10097553 |
| 71 | Unlearnable Clusters: Towards Label-Agnostic Unlearnable Examples | Jiaming Zhang, Xingjun Ma, Qi Yi, Jitao Sang, Yu-Gang Jiang, Yaowei Wang, Changsheng Xu | 2023 | CVPR | https://github.com/jiamingzhang94/Unlearnable-Clusters | https://doi.org/10.1109/CVPR52729.2023.00388 |
| 72 | CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning | Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, Kai-Wei Chang | 2023 | 2023 IEEE/CVF International Conference on Computer Vision (ICCV) | https://github.com/nishadsinghi/CleanCLIP | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10377853 |
| 73 | Tangent Model Composition for Ensembling and Continual Fine-tuning | T. Y. Liu, S. Soatto | 2023 | 2023 IEEE/CVF International Conference on Computer Vision (ICCV) | https://github.com/tianyu139/tangent-model-composition | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10378156 |
| 74 | QoSEraser: A Data Erasable Framework for Web Service QoS Prediction | Y. Zeng, Y. Li, Z. Xia, Z. Du, J. Wang, R. Lian, J. Xu | 2023 | 2023 IEEE International Conference on Software Services Engineering (SSE) | https://github.com/ZengYuXiang7/QoSEraser | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10234360 |
| 75 | Toward Highly-Efficient and Accurate Services QoS Prediction via Machine Unlearning | Yuxiang Zeng, Jianlong Xu, Yuhui Li, Caiyi Chen, Qingcao Dai, Zibo Du | 2023 | IEEE Access | https://github.com/ZengYuXiang7/CADDEraser | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10171348 |
| 76 | Machine Unlearning of Federated Clusters | Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, Olgica Milenkovic | 2023 | ICLR | https://github.com/thupchnsky/mufc | https://openreview.net/pdf?id=VzwfoFyYDga |
| 77 | Knowledge Unlearning for Mitigating Privacy Risks in Language Models | Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, Minjoon Seo | 2023 | ACL | https://github.com/joeljang/knowledge-unlearning | https://doi.org/10.18653/v1/2023.acl-long.805 |
| 78 | Generalizability and Application of the Skin Reflectance Estimate Based on Dichromatic Separation (SREDS) | J. Drahos, R. Plesh, K. Bahmani, M. Banavar, S. Schuckers | 2023 | 2023 International Conference of the Biometrics Special Interest Group (BIOSIG) | https://github.com/JosephDrahos/SREDS | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10345973 |
| 79 | ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer | Shen Lin, Xiaoyu Zhang, Chenyang Chen, Xiaofeng Chen, Willy Susilo | 2023 | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | https://github.com/RUIYUN-ML/ERM-KTP | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10203043 |
| 80 | Deep Regression Unlearning | Ayush Kumar Tarun, Vikram Singh Chundawat, Murari Mandal, Mohan S. Kankanhalli | 2023 | arXiv | https://github.com/ayu987/deep-regression-unlearning | https://doi.org/10.48550/arXiv.2210.08196 |
| 81 | A Survey of Machine Unlearning | Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung Nguyen | 2022-09-06 | arXiv | https://github.com/tamlhp/awesome-machine-unlearning | https://doi.org/10.48550/arXiv.2209.02299 |
| 82 | Deep Unlearning via Randomized Conditionally Independent Hessians | Ronak Mehta, Sourav Pal, Vikas Singh, Sathya N. Ravi | 2022-04-15 | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | https://github.com/vsingh-group/LCODEC-deep-unlearning | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9880362 |
| 83 | Robust Unlearnable Examples: Protecting Data Against Adversarial Learning | Shaopeng Fu, Fengxiang He, Yang Liu, Li Shen, Dacheng Tao | 2022-03-28 | arXiv | https://github.com/fshp971/robust-unlearnable-examples | https://doi.org/10.48550/arXiv.2203.14533 |
| 84 | Continual Learning and Private Unlearning | Bo Liu, Qiang Liu, Peter Stone | 2022-03-24 | CoLLAs | https://github.com/Cranial-XIX/Continual-Learning-Private-Unlearning | https://proceedings.mlr.press/v199/liu22a.html |
| 85 | FedHarmony: Unlearning Scanner Bias with Distributed Data | Nicola K. Dinsdale, Mark Jenkinson, Ana I. L. Namburete | 2022-01-01 | MICCAI | https://github.com/nkdinsdale/FedHarmony | https://doi.org/10.1007/978-3-031-16452-1_66 |
| 86 | When Machine Unlearning Jeopardizes Privacy | Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, Yang Zhang | 2021-11 | CCS '21: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security | https://github.com/MinChen00/UnlearningLeaks | https://dl.acm.org/doi/10.1145/3460120.3484756 |
| 87 | Towards Probabilistic Verification of Machine Unlearning | David Marco Sommer, Liwei Song, Sameer Wagh, Prateek Mittal | 2020-03-09 | arXiv | https://github.com/inspire-group/unlearning-verification | https://arxiv.org/abs/2003.04247 |
| 88 | Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery | S. Chen, Y. Meng, H. Tang, Y. Tian, N. He, C. Shao | 2020 | IEEE/ASME Transactions on Mechatronics | https://github.com/siyuanc2/machine-fault-diag | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9136730 |
| 89 | Compressed Sensing with Deep Image Prior and Learned Regularization | Dave Van Veen, Ajil Jalal, Mahdi Soltanolkotabi, Eric Price, Sriram Vishwanath, Alexandros G. Dimakis | nan | OpenReview | https://github.com/anon-iclr/csdip-iclr | https://openreview.net/pdf/960068efdade58de64b1b641bcccfdba53ac168b.pdf |
| 90 | Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples | Weixin Chen, Baoyuan Wu, Haoqian Wang | nan | NeurIPS 2022 Accept | https://github.com/SCLBD/Effective_backdoor_defense | https://openreview.net/pdf/82397e777241ae042276e8493ca8e5d228821582.pdf |
| 91 | Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models | Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro | nan | NeurIPS 2022 Accept | https://github.com/NVIDIA/Megatron-LM/ | https://openreview.net/pdf/cbffa1a0bf2612f146adbc70397e00fc131d2db4.pdf |
| 92 | Knowledge Removal in Sampling-based Bayesian Inference | Shaopeng Fu, Fengxiang He, Dacheng Tao | nan | ICLR 2022 Poster | https://github.com/fshp971/mcmc-unlearning | https://openreview.net/pdf/a42ad90a502167268f1ba4c67f57150bf59ccbc9.pdf |
| 93 | Training wide residual networks for deployment using a single bit for each weight | Mark D. McDonnell | nan | OpenReview | https://github.com/McDonnell-Lab/1-bit-per-weight/ | https://openreview.net/pdf/861cb006a62eb71925571a5d4979901d047a92ea.pdf |
| 94 | Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork | Haotao Wang, Junyuan Hong, Aston Zhang, Jiayu Zhou, Zhangyang Wang | nan | NeurIPS 2022 Accept | https://github.com/VITA-Group/Trap-and-Replace-Backdoor-Defense | https://openreview.net/pdf/a0b040b733099d83fd30969cd35fa8cc35c367b2.pdf |