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Machine unlearning is the task of updating machine learning (ML) models after a subset of the training data they were trained on is deleted.
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto. 2020a · 1911
Earlier work this paper cites.
Certified Data Removal from Machine Learning Models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens van der Maaten. 2020 · 1911
Earlier work this paper cites.
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2020 · 1912
Earlier work this paper cites.
Incremental and Decremental Support Vector Machine Learning. In NIPS
Tomaso A Poggio. 2000 · 2000
Earlier work this paper cites.
A Parallel Mixture of SVMs for Very Large Scale Problems
Ronan Collobert, Samy Bengio, and Yoshua Bengio. 2002 · 2002
Earlier work this paper cites.
Aditya Golatkar, Alessandro Achille, and Stefano Soatto. 2020b · 2003
Earlier work this paper cites.
Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe. 2004 · 2004
Earlier work this paper cites.
DeltaGrad: Rapid Retraining of Machine Learning Models
Yinjun Wu, Edgar Dobriban, and Susan B. Davidson. 2020a · 2006
Earlier work this paper cites.
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi. 2020 · 2007
Earlier work this paper cites.
Privacy-preserving logistic regression. In Advances in Neural Information Processing Systems , D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou (Eds.), Vol. 21. Curran Associates, Inc
Kamalika Chaudhuri and Claire Monteleoni. 2009 · 2008
Earlier work this paper cites.
Effective and Efficient Multilabel Classification in Domains with Large Number of Labels
G. Tsoumakas, I. Katakis, and I. Vlahavas. 2008 · 2008
Earlier work this paper cites.
Machine Unlearning for Random Forests
Jonathan Brophy and Daniel Lowd. 2021 · 2009
Cited alongside, same era.
Laura Graves, Vineel Nagisetty, and Vijay Ganesh. 2020 · 2010
Cited alongside, same era.
Multiple Incremental Decremental Learning of Support Vector Machines
M. Karasuyama and I. Takeuchi. 2010 · 2010
Cited alongside, same era.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes. 2010 · 2010
Cited alongside, same era.
LIBSVM: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin. 2011 · 2011
Cited alongside, same era.
Towards Making Systems Forget with Machine Unlearning. In 2015 IEEE Symposium on Security and Privacy . IEEE, San Jose, CA, 463–480
Yinzhi Cao and Junfeng Yang. 2015 · 2015
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Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Later among the works it cites.
Approximate Data Deletion from Machine Learning Models. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research) , Arindam Banerjee and Kenji Fukumizu (Eds.), Vol. 130. PMLR, 2008–2016
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, and James Zou. 2021 · 2016
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Regulation (EU) 2016/679
Council of European Union. 2016 · 2016
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Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto. 2020c · 2012
Cited alongside, same era.
The EU Proposal for a General Data Protection Regulation and the roots of the “right to be forgotten”
Alessandro Mantelero. 2013 · 2013
Cited alongside, same era.
Searching for exotic particles in high-energy physics with deep learning
P. Baldi, P. Sadowski, and D. Whiteson. 2014 · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Cited alongside, same era.
Incremental and Decremental Training for Linear Classification. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’14) . Association for Computing Machinery, New York, New York, USA, 343–352
Cheng-Hao Tsai, Chieh-Yen Lin, and Chih-Jen Lin. 2014 · 2014
Cited alongside, same era.
CIFAR-10 (Canadian Institute for Advanced Research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. [n.d.]
Cited in the paper.
Variational Bayesian Unlearning
Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet. [n.d.]
Cited in the paper.
Pang Wei Koh and Percy Liang. 2017 · 2017
Later among the works it cites.
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 · 2019
Later among the works it cites.
New Insights and Perspectives on the Natural Gradient Method
James Martens. 2020 · 2020
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“Amnesia” – Towards Machine Learning Models That Can Forget User Data Very Fast. In Conference on Innovative Data Systems Research (CIDR) . 4
Sebastian Schelter. 2020 · 2020
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PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data . ACM, Portland OR USA, 447–462
Yinjun Wu, Val Tannen, and Susan B. Davidson. 2020b · 2020
Later among the works it cites.
Certifiable Machine Unlearning for Linear Models
Ananth Mahadevan and Michael Mathioudakis. 2021 · 2021
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