Fetching the paper…
Reading the bibliography…
Machine unlearning (MU) aims to remove the influence of specific data from trained models, addressing privacy concerns and ensuring compliance with regulations such as the ``right to be forgotten.'' Evaluating strong unlearning, where the unlearned model is indistinguishable from one retrained without the forgetting data, remains a significant challenge in deep neural networks (DNNs).
A mathematical theory of communication
C. E. Shannon · 1948
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
R. M. French · 1999
Earlier work this paper cites.
The information bottleneck method
N. Tishby, F. C. Pereira, and W. Bialek · 2000
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Y. Cao and J. Yang · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
Deep learning and the information bottleneck principle
N. Tishby and N. Zaslavsky · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
P. Voigt and A. Von dem Bussche · 2017
Earlier work this paper cites.
Representation learning with contrastive predictive coding
A. v. d. Oord, Y. Li, and O. Vinyals · 2018
Earlier work this paper cites.
Making ai forget you: Data deletion in machine learning
A. Ginart, M. Guan, G. Valiant, and J. Y. Zou · 2019
Earlier work this paper cites.
Certified data removal from machine learning models
C. Guo, T. Goldstein, A. Hannun, and L. Van Der Maaten · 2019
Earlier work this paper cites.
On variational bounds of mutual information
B. Poole, S. Ozair, A. Van Den Oord, A. Alemi, and G. Tucker · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
Earlier work this paper cites.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
A. Golatkar, A. Achille, and S. Soatto · 2020
Cited alongside, same era.
Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
A. Golatkar, A. Achille, and S. Soatto · 2020
Cited alongside, same era.
Supervised contrastive learning
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan · 2020
Cited alongside, same era.
On hyperparameter optimization of machine learning algorithms: Theory and practice
L. Yang and A. Shami · 2020
Cited alongside, same era.
Coded machine unlearning
N. Aldaghri, H. Mahdavifar, and A. Beirami · 2021
Cited alongside, same era.
Machine unlearning
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot · 2021
Cited alongside, same era.
Unrolling sgd: Understanding factors influencing machine unlearning
A. Thudi, G. Deza, V. Chandrasekaran, and N. Papernot · 2022
Later among the works it cites.
On the necessity of auditable algorithmic definitions for machine unlearning
A. Thudi, H. Jia, I. Shumailov, and N. Papernot · 2022
Later among the works it cites.
Arcane: An efficient architecture for exact machine unlearning
H. Yan, X. Li, Z. Guo, H. Li, F. Li, and X. Lin · 2022
Later among the works it cites.
Boundary unlearning: Rapid forgetting of deep networks via shifting the decision boundary
M. Chen, W. Gao, G. Liu, K. Peng, and C. Wang · 2023
Later among the works it cites.
Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher
V. S. Chundawat, A. K. Tarun, M. Mandal, and M. Kankanhalli · 2023
Later among the works it cites.
Zero-shot machine unlearning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Machine unlearning for random forests
J. Brophy and D. Lowd · 2021
Cited alongside, same era.
Mixed-privacy forgetting in deep networks
A. Golatkar, A. Achille, A. Ravichandran, M. Polito, and S. Soatto · 2021
Cited alongside, same era.
Scaling up visual and vision-language representation learning with noisy text supervision
C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig · 2021
Cited alongside, same era.
Descent-to-delete: Gradient-based methods for machine unlearning
S. Neel, A. Roth, and S. Sharifi-Malvajerdi · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
Cited alongside, same era.
Remember what you want to forget: Algorithms for machine unlearning
A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh · 2021
Cited alongside, same era.
V. S. Chundawat, A. K. Tarun, M. Mandal, and M. Kankanhalli · 2023
Later among the works it cites.
C. Fan, J. Liu, Y. Zhang, D. Wei, E. Wong, and S. Liu · 2023
Later among the works it cites.
Tight bounds for machine unlearning via differential privacy
Y. Huang and C. L. Canonne · 2023
Later among the works it cites.
Exploring the landscape of machine unlearning: A survey and taxonomy
T. Shaik, X. Tao, H. Xie, L. Li, X. Zhu, and Q. Li · 2023
Later among the works it cites.
Label-agnostic forgetting: A supervision-free unlearning in deep models
S. Shen, C. Zhang, Y. Zhao, A. Bialkowski, W. T. Chen, and M. Xu · 2023
Later among the works it cites.
Deep regression unlearning
A. K. Tarun, V. S. Chundawat, M. Mandal, and M. Kankanhalli · 2023
Later among the works it cites.
Fast yet effective machine unlearning
A. K. Tarun, V. S. Chundawat, M. Mandal, and M. Kankanhalli · 2023
Later among the works it cites.
Fast machine unlearning without retraining through selective synaptic dampening
J. Foster, S. Schoepf, and A. Brintrup · 2024
Closest in time.
Inexact unlearning needs more careful evaluations to avoid a false sense of privacy
J. Hayes, I. Shumailov, E. Triantafillou, A. Khalifa, and N. Papernot · 2024
Closest in time.
Layer attack unlearning: Fast and accurate machine unlearning via layer level attack and knowledge distillation
H. Kim, S. Lee, and S. S. Woo · 2024
Closest in time.
Towards unbounded machine unlearning
M. Kurmanji, P. Triantafillou, J. Hayes, and E. Triantafillou · 2024
Closest in time.
Model sparsity can simplify machine unlearning
J. Liu, P. Ram, Y. Yao, G. Liu, Y. Liu, P. SHARMA, S. Liu, et al · 2024
Closest in time.
Contrastive unlearning: A contrastive approach to machine unlearning
Q. Zhang, C. Yang, J. Lou, L. Xiong, et al · 2024
Closest in time.