Fetching the paper…
Reading the bibliography…
Removing information from a machine learning model is a non-trivial task that requires to partially revert the training process.
F. Hampel, “The influence curve and its role in robust estimation,” in
1974
Earlier work this paper cites.
R. D. Cook and S. Weisberg, “Residuals and influence in regression,”
1982
Earlier work this paper cites.
Y. LeCun, J. Denker, and S. Solla, “Optimal brain damage,” in
1990
Earlier work this paper cites.
B. Hassibi, D. Stork, and G. Wolff, “Optimal brain surgeon: Extensions and performance comparisons,” in
1994
Earlier work this paper cites.
B. A. Pearlmutter, “Fast exact multiplication by the hessian,”
1994
Earlier work this paper cites.
D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,”
1997
Earlier work this paper cites.
R. O. Duda, P. E. Hart, and D. G. Stork,
2000
Earlier work this paper cites.
S. Boyd and L. Vandenberghe,
2004
Earlier work this paper cites.
C. Dwork, “Differential privacy,” in
2006
Earlier work this paper cites.
V. Metsis, G. Androutsopoulos, and G. Paliouras, “Spam filtering with naive bayes - which naive bayes?” in
2006
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in
2006
Earlier work this paper cites.
J. Attenberg, K. Weinberger, A. Dasgupta, A. Smola, and M. Zinkevich, “Collaborative email-spam filtering with the hashing trick,” in
2009
Earlier work this paper cites.
C. Dwork and J. Lei, “Differential privacy and robust statistics,” in
2009
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” University of Toronto, Tech. Rep., 2009
2009
Earlier work this paper cites.
C. Dwork, G. N. Rothblum, and S. Vadhan, “Boosting and differential privacy,” in
2010
Earlier work this paper cites.
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate, “Differentially private empirical risk minimization,”
2011
Earlier work this paper cites.
I. Sutskever, J. Martens, and G. Hinton, “Generating text with recurrent neural networks,” in
2011
Earlier work this paper cites.
D. Kifer and A. Machanavajjhala, “No free lunch in data privacy,” in
2011
Earlier work this paper cites.
H. Xiao, H. Xiao, and C. Eckert, “Adversarial label flips attack on support vector machines,” in
2012
Earlier work this paper cites.
2013
Cited alongside, same era.
D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck, “Drebin: Efficient and explainable detection of Android malware in your pocket,” University of Göttingen, Tech. Rep. IFI-TB-2013-02, Aug. 2013
2013
Cited alongside, same era.
C. Dwork and A. Roth, “The algorithmic foundations of differential privacy,”
2014
Cited alongside, same era.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in
2015
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in
2015
Cited alongside, same era.
M.-E. Brunet, C. Alkalay-Houlihan, A. Anderson, and R. Zemel, “Understanding the origins of bias in word embeddings,” in
2019
Later among the works it cites.
P. Schulam and S. Saria, “Can you trust this prediction? auditing pointwise reliability after learning,” in
2019
Later among the works it cites.
C. Guo, T. Goldstein, A. Y. Hannun, and L. van der Maaten, “Certified data removal from machine learning models,” in
2020
Later among the works it cites.
S. Zanella Béguelin, L. Wutschitz, S. Tople, V. Rühle, A. Paverd, O. Ohrimenko, B. Köpf, and M. Brockschmidt, “Analyzing Information Leakage of Updates to Natural Language Models,” in
2020
Later among the works it cites.
K. Leino and M. Fredrikson, “Stolen memories: Leveraging model memorization for calibrated white-box membership inference,” in
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data,”
2016
Cited alongside, same era.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in
2016
Cited alongside, same era.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models.” in
2017
Cited alongside, same era.
D. Dua and C. Graff, “UCI machine learning repository. diabetis data set.” 2017. [Online]. Available:
2017
Cited alongside, same era.
——, “UCI machine learning repository. census income data set.” 2017. [Online]. Available:
2017
Cited alongside, same era.
N. Agarwal, B. Bullins, and E. Hazan, “Second-order stochastic optimization for machine learning in linear time,”
2017
Cited alongside, same era.
H. Chen, S. Si, Y. Li, C. Chelba, S. Kumar, D. Boning, and C.-J. Hsieh, “Multi-stage influence function,” in
2020
Later among the works it cites.
S. Basu, X. You, and S. Feizi, “On second-order group influence functions for black-box predictions,” in
2020
Later among the works it cites.
E. Barshan, M. Brunet, and G. Dziugaite, “Relatif: Identifying explanatory training examples via relative influence,” in
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Golatkar, A. Achille, and S. Soatto, “Eternal sunshine of the spotless net: Selective forgetting in deep networks,” in
2020
Later among the works it cites.
D. Desfontaines and B. Pejó, “Sok: Differential privacies,”
2020
Later among the works it cites.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” in
2021
Closest in time.
S. Neel, A. Roth, and S. Sharifi-Malvajerdi, “Descent-to-delete: Gradient-based methods for machine unlearning,” in
2021
Closest in time.
N. Aldaghri, H. Mahdavifar, and A. Beirami, “Coded machine unlearning,”
2021
Closest in time.
N. Carlini, F. Tramèr, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, and A. Roberts, “Extracting training data from large language models,” in
2021
Closest in time.
E. De Cristofaro, “A critical overview of privacy in machine learning,”
2021
Closest in time.
S. Basu, P. Pope, and S. Feizi, “Influence functions in deep learning are fragile,” in
2021
Closest in time.
A. Golatkar, A. Achille, A. Ravichandran, M. Polito, and S. Soatto, “Mixed-privacy forgetting in deep networks,” in
2021
Closest in time.