Fetching the paper…
Reading the bibliography…
Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted.
1907
Earlier work this paper cites.
J. H. Saltzer and M. D. Schroeder, “The protection of information in computer systems,” Proceedings of the IEEE , vol. 63, no. 9, pp. 1278–1308, 1975
1975
Earlier work this paper cites.
R. D. Cook and S. Weisberg, “Characterizations of an empirical influence function for detecting influential cases in regression,” Technometrics , vol. 22, no. 4, pp. 495–508, 1980
1980
Earlier work this paper cites.
L. G. Valiant, “A theory of the learnable,” in Proceedings of the sixteenth annual ACM symposium on Theory of computing . ACM, 1984, pp. 436–445
1984
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature , vol. 323, no. 6088, pp. 533–536, 1986
1986
Earlier work this paper cites.
M. Kearns, “Thoughts on hypothesis boosting,” Unpublished manuscript , vol. 45, p. 105, 1988
1988
Earlier work this paper cites.
Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of computer and system sciences , vol. 55, no. 1, pp. 119–139, 1997
1997
Earlier work this paper cites.
M. Kearns, “Efficient noise-tolerant learning from statistical queries,” Journal of the ACM (JACM) , vol. 45, no. 6, pp. 983–1006, 1998
1998
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, pp. 2278 – 2324, 12 1998
1998
Earlier work this paper cites.
D. Opitz and R. Maclin, “Popular ensemble methods: An empirical study,” Journal of artificial intelligence research , vol. 11, pp. 169–198, 1999
1999
Earlier work this paper cites.
R. E. Schapire, “A brief introduction to boosting,” in Ijcai , vol. 99, 1999, pp. 1401–1406
1999
Earlier work this paper cites.
T. G. Dietterich, “Ensemble methods in machine learning,” in International workshop on multiple classifier systems . Springer, 2000, pp. 1–15
2000
Earlier work this paper cites.
H. Schwenk and Y. Bengio, “Boosting neural networks,” Neural computation , vol. 12, no. 8, pp. 1869–1887, 2000
2000
Earlier work this paper cites.
B. Nelson, M. Barreno, F. J. Chi, A. D. Joseph et al. , “Exploiting machine learning to subvert your spam filter,” in Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats . USENIX Association, 2008
2008
Earlier work this paper cites.
B. I. Rubinstein, B. Nelson, L. Huang, A. D. Joseph, S.-h. Lau, S. Rao, N. Taft, and J. D. Tygar, “Antidote: Understanding and defending against poisoning of anomaly detectors,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement , 2009
2009
Earlier work this paper cites.
K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,” in Advances in neural information processing systems , 2009, pp. 289–296
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
B. Settles, “Active learning literature survey,” University of Wisconsin-Madison Department of Computer Sciences, Tech. Rep., 2009
2009
Earlier work this paper cites.
S.-J. Huang, R. Jin, and Z.-H. Zhou, “Active learning by querying informative and representative examples,” in Advances in neural information processing systems , 2010, pp. 892–900
2010
Earlier work this paper cites.
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate, “Differentially private empirical risk minimization,” Journal of Machine Learning Research , vol. 12, no. Mar, pp. 1069–1109, 2011
2011
Cited alongside, same era.
C. Dwork, “Differential privacy,” Encyclopedia of Cryptography and Security , pp. 338–340, 2011
2011
Cited alongside, same era.
S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter, “Pegasos: Primal estimated sub-gradient solver for svm,” Mathematical programming , vol. 127, no. 1, pp. 3–30, 2011
2011
Cited alongside, same era.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Ng, “Reading digits in natural images with unsupervised feature learning,” NIPS , 01 2011
2011
Cited alongside, same era.
S. Shalev-Shwartz et al. , “Online learning and online convex optimization,” Foundations and Trends® in Machine Learning , vol. 4, no. 2, pp. 107–194, 2012
2017
Later among the works it cites.
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers et al. , “In-datacenter performance analysis of a tensor processing unit,” in 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) . IEEE, 2017, pp. 1–12
2017
Later among the works it cites.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 1885–1894
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2012
Cited alongside, same era.
2012
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Cited alongside, same era.
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, M. Ranzato, A. Senior et al. , “Large scale distributed deep networks,” in Advances in neural information processing systems , 2012
2012
Cited alongside, same era.
S. T. Setty, R. McPherson, A. J. Blumberg, and M. Walfish, “Making argument systems for outsourced computation practical (sometimes).” in NDSS , vol. 1, no. 9, 2012, p. 17
2012
Cited alongside, same era.
A. Mantelero, “The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’,” Computer Law & Security Review , vol. 29, no. 3, pp. 229–235, 2013
2013
Cited alongside, same era.
X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers et al. , “Practical lessons from predicting clicks on ads at facebook,” in Proceedings of the Eighth International Workshop on Data Mining for Online Advertising . ACM, 2014, pp. 1–9
2014
Cited alongside, same era.
C. Dwork, A. Roth et al. , “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Cited alongside, same era.
2017
Later among the works it cites.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Advances in neural information processing systems , 2017, pp. 4077–4087
2017
Later among the works it cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 3–18
2017
Later among the works it cites.
2017
Later among the works it cites.
C. Tan, L. Yu, J. B. Leners, and M. Walfish, “The efficient server audit problem, deduplicated re-execution, and the web,” in Proceedings of the 26th Symposium on Operating Systems Principles . ACM, 2017, pp. 546–564
2017
Later among the works it cites.
O. of the Privacy Commissioner of Canada, “Announcement: Privacy commissioner seeks federal court determination on key issue for canadians’ online reputation,” https://www.priv.gc.ca/en/opc-news/news-and-announcements/2018/an_181010/ , Oct 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2019
Closest in time.
S. Shastri, M. Wasserman, and V. Chidambaram, “The seven sins of personal-data processing systems under gdpr,” USENIX HotCloud , 2019
2019
Closest in time.
N. Carlini, C. Liu, U. Erlingsson, J. Kos, and D. Song, “The secret sharer: Evaluating and testing unintended memorization in neural networks,” in Proceedings of the 28th USENIX Conference on Security Symposium . USENIX Association, 2019
2019
Closest in time.
T. Bertram, E. Bursztein, S. Caro, H. Chao, R. C. Feman et al. , “Five years of the right to be forgotten,” in Proceedings of the Conference on Computer and Communications Security , 2019
2019
Closest in time.
2019
Closest in time.
T. Ben-Nun and T. Hoefler, “Demystifying parallel and distributed deep learning: An in-depth concurrency analysis,” ACM Computing Surveys (CSUR) , vol. 52, no. 4, p. 65, 2019
2019
Closest in time.
C. O. Sakar, S. O. Polat, M. Katircioglu, and Y. Kastro, “Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and lstm recurrent neural networks,” Neural Computing and Applications , vol. 31, no. 10, pp. 6893–6908, 2019
2019
Closest in time.
A. Golatkar, A. Achille, and S. Soatto, “Eternal sunshine of the spotless net: Selective forgetting in deep networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9304–9312
2020
Closest in time.
R. S. Wahby, Y. Ji, A. J. Blumberg, A. Shelat, J. Thaler, M. Walfish, and T. Wies, “Full accounting for verifiable outsourcing,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 2071–2086
2086
Closest in time.