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Machine unlearning has raised significant interest with the adoption of laws ensuring the ``right to be forgotten''.
T. H. Gronwall, “Note on the derivatives with respect to a parameter of the solutions of a system of differential equations,” Annals of Mathematics
1919
Earlier work this paper cites.
W. K. Hastings, “Monte carlo sampling methods using markov chains and their applications,” 1970
1970
Earlier work this paper cites.
L. Gross, “Logarithmic sobolev inequalities,” American Journal of Mathematics
1975
Earlier work this paper cites.
DOI: https://doi.org/10.24432/C5XW20
B. Becker and R. Kohavi, “Adult.” UCI Machine Learning Repository, 1996 · 1996
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006. Proceedings 3
2006
Earlier work this paper cites.
A. Krizhevsky et al
2009
Earlier work this paper cites.
R. M. Neal et al
2011
Earlier work this paper cites.
A. S. Dalalyan and A. B. Tsybakov, “Sparse regression learning by aggregation and langevin monte-carlo,” Journal of Computer and System Sciences
2012
Earlier work this paper cites.
Springer Science & Business Media, 2012
S. P. Meyn and R. L. Tweedie, Markov chains and stochastic stability · 2012
Earlier work this paper cites.
L. Deng, “The mnist database of handwritten digit images for machine learning research,” IEEE Signal Processing Magazine
2012
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in 2015 IEEE symposium on security and privacy
2015
Earlier work this paper cites.
P. Kairouz, S. Oh, and P. Viswanath, “The composition theorem for differential privacy,” in International conference on machine learning
2015
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
M. Hardt, B. Recht, and Y. Singer, “Train faster, generalize better: Stability of stochastic gradient descent,” in International conference on machine learning
2016
Earlier work this paper cites.
T. maintainers and contributors, “Torchvision: Pytorch’s computer vision library.” https://github.com/pytorch/vision , 2016
2016
Earlier work this paper cites.
I. Mironov, “Rényi differential privacy,” in 2017 IEEE 30th computer security foundations symposium (CSF)
2017
Earlier work this paper cites.
A. Durmus and E. Moulines, “Nonasymptotic convergence analysis for the unadjusted langevin algorithm,” 2017
2017
Earlier work this paper cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
V. Feldman, I. Mironov, K. Talwar, and A. Thakurta, “Privacy amplification by iteration,” in 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS)
2018
Cited alongside, same era.
N. Carlini, C. Liu, Ú. Erlingsson, J. Kos, and D. Song, “The secret sharer: Evaluating and testing unintended memorization in neural networks,” in 28th USENIX Security Symposium (USENIX Security 19)
2019
Cited alongside, same era.
S. Vempala and A. Wibisono, “Rapid convergence of the unadjusted langevin algorithm: Isoperimetry suffices,” Advances in neural information processing systems
2019
Cited alongside, same era.
Y.-A. Ma, Y. Chen, C. Jin, N. Flammarion, and M. I. Jordan, “Sampling can be faster than optimization,” Proceedings of the National Academy of Sciences
H.-B. Chen, S. Chewi, and J. Niles-Weed, “Dimension-free log-sobolev inequalities for mixture distributions,” Journal of Functional Analysis
2021
Later among the works it cites.
S. Fu, F. He, Y. Xu, and D. Tao, “Bayesian inference forgetting,” arXiv preprint arXiv:2101.06417
2021
Later among the works it cites.
P. Kairouz, B. McMahan, S. Song, O. Thakkar, A. Thakurta, and Z. Xu, “Practical and private (deep) learning without sampling or shuffling,” in International Conference on Machine Learning
2021
Later among the works it cites.
E. Chien, C. Pan, and O. Milenkovic, “Efficient model updates for approximate unlearning of graph-structured data,” in The Eleventh International Conference on Learning Representations
2022
Later among the works it cites.
J. Ye and R. Shokri, “Differentially private learning needs hidden state (or much faster convergence),” Advances in Neural Information Processing Systems
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2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
C. Guo, T. Goldstein, A. Hannun, and L. Van Der Maaten, “Certified data removal from machine learning models,” in International Conference on Machine Learning
2020
Cited alongside, same era.
A. Ganesh and K. Talwar, “Faster differentially private samplers via rényi divergence analysis of discretized langevin mcmc,” Advances in Neural Information Processing Systems
2020
Cited alongside, same era.
Q. P. Nguyen, B. K. H. Low, and P. Jaillet, “Variational bayesian unlearning,” Advances in Neural Information Processing Systems
2020
Cited alongside, same era.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” 2020
2020
Cited alongside, same era.
A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh, “Remember what you want to forget: Algorithms for machine unlearning,” Advances in Neural Information Processing Systems
2021
Cited alongside, same era.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” in 2021 IEEE Symposium on Security and Privacy (SP)
2021
Cited alongside, same era.
2022
Later among the works it cites.
J. Altschuler and K. Talwar, “Privacy of noisy stochastic gradient descent: More iterations without more privacy loss,” Advances in Neural Information Processing Systems
2022
Later among the works it cites.
2022
Later among the works it cites.
M. A. Erdogdu, R. Hosseinzadeh, and S. Zhang, “Convergence of langevin monte carlo in chi-squared and rényi divergence,” in International Conference on Artificial Intelligence and Statistics
2022
Later among the works it cites.
2022
Later among the works it cites.
Q. P. Nguyen, R. Oikawa, D. M. Divakaran, M. C. Chan, and B. K. H. Low, “Markov chain monte carlo-based machine unlearning: Unlearning what needs to be forgotten,” in Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security
2022
Later among the works it cites.
2022
Later among the works it cites.
E. Ullah and R. Arora, “From adaptive query release to machine unlearning,” in International Conference on Machine Learning
2023
Later among the works it cites.
R. Chourasia and N. Shah, “Forget unlearning: Towards true data-deletion in machine learning,” in International Conference on Machine Learning
2023
Later among the works it cites.
Online; accessed September 29, 2023
Chewi, Sinho, “Log-Concave Sampling.” https://chewisinho.github.io/main.pdf , 2023 · 2023
Later among the works it cites.
2023
Later among the works it cites.
E. Chien, H. Wang, Z. Chen, and P. Li, “Stochastic gradient langevin unlearning,” Advances in Neural Information Processing Systems
2024
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