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Recent advancements in Machine Unlearning (MU) have introduced solutions to selectively remove certain training samples, such as those with outdated or sensitive information, from trained models.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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Ucf101: A dataset of 101 human actions classes from videos in the wild, 2012
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Tiny imagenet visual recognition challenge
Ya Le and Xuan S. Yang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, and Rob Fergus · 2018
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Speech commands: A dataset for limited-vocabulary speech recognition, 2018
Pete Warden · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi · 2019
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli · 2020
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Superloss: A generic loss for robust curriculum learning
Thibault Castells, Philippe Weinzaepfel, and Jerome Revaud · 2020
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Electra: Pre-training text encoders as discriminators rather than generators, 2020
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
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Variational bayesian unlearning
Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet · 2020
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2020
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Machine unlearning for random forests
Jonathan Brophy and Daniel Lowd · 2021
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Graph unlearning
Min Chen, Zhikun Zhang, Tianhao Wang, Michael Backes, Mathias Humbert, and Yang Zhang · 2021
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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
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Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
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Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
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Hubert: Self-supervised speech representation learning by masked prediction of hidden units
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed · 2021
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Approximate data deletion from machine learning models
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision, 2021
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Descent-to-delete: Gradient-based methods for machine unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
Cited alongside, same era.
Hedgecut: Maintaining randomised trees for low-latency machine unlearning
Sebastian Schelter, Stefan Grafberger, and Ted Dunning · 2021
Cited alongside, same era.
Machine unlearning via algorithmic stability
Enayat Ullah, Tung Mai, Anup Rao, Ryan A. Rossi, and Raman Arora · 2021
Cited alongside, same era.
Recommendation unlearning
Chong Chen, Fei Sun, Min Zhang, and Bolin Ding · 2022
Cited alongside, same era.
Zero-shot machine unlearning
Vikram S Chundawat, Ayush K Tarun, Murari Mandal, and Mohan Kankanhalli · 2022
Cited alongside, same era.
Preserving privacy through dememorization: An unlearning technique for mitigating memorization risks in language models
Aly Kassem, Omar Mahmoud, and Sherif Saad · 2023
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Ablating concepts in text-to-image diffusion models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, and Jun-Yan Zhu · 2023
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 2023
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Make text unlearnable: Exploiting effective patterns to protect personal data
Xinzhe Li and Ming Liu · 2023
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UltraRE: Enhancing receraser for recommendation unlearning via error decomposition
Yuyuan Li, Chaochao Chen, Yizhao Zhang, Weiming Liu, Lingjuan Lyu, Xiaolin Zheng, Dan Meng, and Jun Wang · 2023
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Towards adversarial evaluations for inexact machine unlearning
Shashwat Goel, Ameya Prabhu, Amartya Sanyal, Ser-Nam Lim, Philip Torr, and Ponnurangam Kumaraguru · 2022
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2022
Cited alongside, same era.
Evaluating pretraining strategies for clinical BERT models
Anastasios Lamproudis, Aron Henriksson, and Hercules Dalianis · 2022
Cited alongside, same era.
Making recommender systems forget: Learning and unlearning for erasable recommendation, 2022
Yuyuan Li, Xiaolin Zheng, Chaochao Chen, and Junlin Liu · 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
Cited alongside, same era.
Learn to forget: Machine unlearning via neuron masking
Zhuo Ma, Yang Liu, Ximeng Liu, Jian Liu, Jianfeng Ma, and Kui Ren · 2022
Cited alongside, same era.
Shen Lin, Xiaoyu Zhang, Chenyang Chen, Xiaofeng Chen, and Willy Susilo · 2023
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FActScore: Fine-grained atomic evaluation of factual precision in long form text generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2023
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Machine unlearning of federated clusters
Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana, and Olgica Milenkovic · 2023
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On the trade-off between actionable explanations and the right to be forgotten
Martin Pawelczyk, Tobias Leemann, Asia Biega, and Gjergji Kasneci · 2023
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Robust speech recognition via large-scale weak supervision
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine Mcleavey, and Ilya Sutskever · 2023
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Large-scale robustness analysis of video action recognition models
Madeline C Schiappa, Naman Biyani, Prudvi Kamtam, Shruti Vyas, Hamid Palangi, Vibhav Vineet, and Yogesh Rawat · 2023
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Distill to delete: Unlearning in graph networks with knowledge distillation
Yash Sinha, Murari Mandal, and Mohan Kankanhalli · 2023
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Exploring the impact of model scaling on parameter-efficient tuning
Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
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A boundary offset prediction network for named entity recognition
Minghao Tang, Yongquan He, Yongxiu Xu, Hongbo Xu, Wenyuan Zhang, and Yang Lin · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Fast yet effective machine unlearning
Ayush K. Tarun, Vikram S. Chundawat, Murari Mandal, and Mohan Kankanhalli · 2023
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KGA: A general machine unlearning framework based on knowledge gap alignment
Lingzhi Wang, Tong Chen, Wei Yuan, Xingshan Zeng, Kam-Fai Wong, and Hongzhi Yin · 2023
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Shared adversarial unlearning: Backdoor mitigation by unlearning shared adversarial examples
Shaokui Wei, Mingda Zhang, Hongyuan Zha, and Baoyuan Wu · 2023
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Certified edge unlearning for graph neural networks
Kun Wu, Jie Shen, Yue Ning, Ting Wang, and Wendy Hui Wang · 2023
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Composing parameter-efficient modules with arithmetic operation
Jinghan Zhang, shiqi chen, Junteng Liu, and Junxian He · 2023
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Static and sequential malicious attacks in the context of selective forgetting
CHENXU ZHAO, Wei Qian, Zhitao Ying, and Mengdi Huai · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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To each (textual sequence) its own: Improving memorized-data unlearning in large language models
George-Octavian Barbulescu and Peter Triantafillou · 2024
Closest in time.
Learning to unlearn: Instance-wise unlearning for pre-trained classifiers
Sungmin Cha, Sungjun Cho, Dasol Hwang, Honglak Lee, Taesup Moon, and Moontae Lee · 2024
Closest in time.
Erasing concepts from text-to-image diffusion models with few-shot unlearning, 2024
Masane Fuchi and Tomohiro Takagi · 2024
Closest in time.
Unified concept editing in diffusion models
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov, Joanna Materzyńska, and David Bau · 2024
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Learn to unlearn for deep neural networks: Minimizing unlearning interference with gradient projection
Tuan Hoang, Santu Rana, Sunil Gupta, and Svetha Venkatesh · 2024
Closest in time.
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, and Sijia Liu · 2024
Closest in time.
Machine unlearning for document classification
Lei Kang, Mohamed Ali Souibgui, Fei Yang, Lluis Gomez, Ernest Valveny, and Dimosthenis Karatzas · 2024
Closest in time.
Implicit concept removal of diffusion models, 2024
Zhili Liu, Kai Chen, Yifan Zhang, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, and James Kwok · 2024
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Tofu: A task of fictitious unlearning for llms, 2024
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter · 2024
Closest in time.
Safe-clip: Removing nsfw concepts from vision-and-language models, 2024
Samuele Poppi, Tobia Poppi, Federico Cocchi, Marcella Cornia, Lorenzo Baraldi, and Rita Cucchiara · 2024
Closest in time.
Ring-a-bell! how reliable are concept removal methods for diffusion models?
Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, and Chun-Ying Huang · 2024
Closest in time.
Large language model unlearning, 2024
Yuanshun Yao, Xiaojun Xu, and Yang Liu · 2024
Closest in time.
Unlearncanvas: A stylized image dataset to benchmark machine unlearning for diffusion models, 2024
Yihua Zhang, Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jiancheng Liu, Xiaoming Liu, and Sijia Liu · 2024
Closest in time.
SemEval-2013 task 9 : Extraction of drug-drug interactions from biomedical texts (DDIExtraction 2013)
Isabel Segura-Bedmar, Paloma Martínez, and María Herrero-Zazo · 2056
Closest in time.