Fetching the paper…
Reading the bibliography…
Machine Unlearning (MU) is critical for removing private or hazardous information from deep learning models.
Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2020 · 1912
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2010
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al. 2014 · 2014
Earlier work this paper cites.
Learning face representation from scratch
Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. 2014 · 2014
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang. 2015 · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015 · 2015
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Earlier work this paper cites.
Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou. 2020 · 2020
Earlier work this paper cites.
Global-local gcn: Large-scale label noise cleansing for face recognition
Yaobin Zhang, Weihong Deng, Mei Wang, Jiani Hu, Xian Li, Dongyue Zhao, and Dongchao Wen. 2020 · 2020
Earlier work this paper cites.
Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi. 2021 · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
Earlier work this paper cites.
Remember what you want to forget: Algorithms for machine unlearning
Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh. 2021 · 2021
Earlier work this paper cites.
Fast yet effective machine unlearning
Ayush Tarun, Vikram Chundawat, Murari Mandal, and Mohan Kankanhalli. 2021 · 2021
Earlier work this paper cites.
Stylespace analysis: Disentangled controls for stylegan image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman. 2020 · 2021
Earlier work this paper cites.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. 2022 · 2022
Cited alongside, same era.
Continual learning and private unlearning
Bo Liu, Qiang Liu, and Peter Stone. 2022 · 2022
Cited alongside, same era.
Quark: Controllable text generation with reinforced unlearning
Ximing Lu, Sean Welleck, Jack Hessel, Liwei Jiang, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, and Yejin Choi. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Unlearn what you want to forget: Efficient unlearning for LLMs
Jiaao Chen and Diyi Yang. 2023 · 2023
Cited alongside, same era.
Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2023 · 2023
Later among the works it cites.
Unlearning bias in language models by partitioning gradients
Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, and Heng Ji. 2023 · 2023
Later among the works it cites.
The devil is in the details: Stylefeatureeditor for detail-rich stylegan inversion and high quality image editing
Denis Bobkov, Vadim Titov, Aibek Alanov, and Dmitry Vetrov. 2024 · 2024
Closest in time.
Can textual unlearning solve cross-modality safety alignment?
Trishna Chakraborty, Erfan Shayegani, Zikui Cai, Nael B. Abu-Ghazaleh, M. Salman Asif, Yue Dong, Amit Roy-Chowdhury, and Chengyu Song. 2024 · 2024
Closest in time.
Quickdrop: Efficient federated unlearning by integrated dataset distillation
Akash Dhasade, Yaohong Ding, Song Guo, Anne marie Kermarrec, Martijn De Vos, and Leijie Wu. 2024 · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Multidelete for multimodal machine unlearning
Jiali Cheng and Hadi Amiri. 2023 · 2023
Cited alongside, same era.
Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich. 2023 · 2023
Cited alongside, same era.
Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. 2023 · 2023
Cited alongside, same era.
Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang. 2023 · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
Cited alongside, same era.
Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou. 2023 · 2023
Cited alongside, same era.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023 · 2023
Cited alongside, same era.
Closest in time.
Inexact unlearning needs more careful evaluations to avoid a false sense of privacy
Jamie Hayes, Ilia Shumailov, Eleni Triantafillou, Amr Khalifa, and Nicolas Papernot. 2024 · 2024
Closest in time.
Model sparsity can simplify machine unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, and Sijia Liu. 2024 · 2024
Closest in time.
Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou. 2024 · 2024
Closest in time.
Towards safer large language models through machine unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, and Meng Jiang. 2024 · 2024
Closest in time.
Weidi Luo, Siyuan Ma, Xiaogeng Liu, Xiaoyu Guo, and Chaowei Xiao. 2024 · 2024
Closest in time.
Benchmarking vision language model unlearning via fictitious facial identity dataset
Yingzi Ma, Jiongxiao Wang, Fei Wang, Siyuan Ma, Jiazhao Li, Xiujun Li, Furong Huang, Lichao Sun, Bo Li, Yejin Choi, Muhao Chen, and Chaowei Xiao. 2024 · 2024
Closest in time.
TOFU: A task of fictitious unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary Chase Lipton, and J Zico Kolter. 2024 · 2024
Closest in time.
Grok-1.5 vision preview
x.ai. 2024 · 2024
Closest in time.
Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An, Weihao Chen, Chunhui Li, Jianbing Zhang, and Xinyu Dai. 2024 · 2024
Closest in time.