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A surprising phenomenon in modern machine learning is the ability of a highly overparameterized model to generalize well (small error on the test data) even when it is trained to memorize the training data (zero error on the training data).
A. Rahimi and B. Recht, “Random features for large-scale kernel machines,” in Proceedings of the 20th International Conference on Neural Information Processing Systems
2007
Earlier work this paper cites.
C. Dwork, “Differential privacy: A survey of results,” in International Conference on Theory and Applications of Models of Computation
2008
Earlier work this paper cites.
R. Sarathy and K. Muralidhar, “Evaluating Laplace noise addition to satisfy differential privacy for numeric data.,” Transactions on Data Privacy
2011
Earlier work this paper cites.
F. Rubio and X. Mestre, “Spectral convergence for a general class of random matrices,” Statistics & Probability Letters
2011
Earlier work this paper cites.
R. D. Cook and L. Forzani, “On the mean and variance of the generalized inverse of a singular Wishart matrix,” Electronic Journal of Statistics
2011
Earlier work this paper cites.
PhD thesis, University of California, San Diego, 2012
Y. Cho, Kernel Methods for Deep Learning · 2012
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in IEEE Symposium on Security and Privacy
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in IEEE 31st Computer Security Foundations Symposium
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Bun, J. Ullman, and S. Vadhan, “Fingerprinting codes and the price of approximate differential privacy,” SIAM Journal on Computing
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Belkin, D. Hsu, S. Ma, and S. Mandal, “Reconciling modern machine-learning practice and the classical bias–variance trade-off,” Proceedings of the National Academy of Sciences
2019
Earlier work this paper cites.
A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, and H. Jégou, “White-box vs black-box: Bayes optimal strategies for membership inference,” in International Conference on Machine Learning
2019
Cited alongside, same era.
K. Leino and M. Fredrikson, “Stolen memories: Leveraging model memorization for calibrated white-box membership inference,” in 29th USENIX Security Symposium
2020
Cited alongside, same era.
2020
Cited alongside, same era.
M. Belkin, D. Hsu, and J. Xu, “Two models of double descent for weak features,” SIAM Journal on Mathematics of Data Science
2020
Cited alongside, same era.
P. L. Bartlett, P. M. Long, G. Lugosi, and A. Tsigler, “Benign overfitting in linear regression,” Proceedings of the National Academy of Sciences
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
E. Dobriban and Y. Sheng, “Wonder: Weighted one-shot distributed ridge regression in high dimensions.,” Journal of Machine Learning Research
2020
Cited alongside, same era.
N. Carlini, F. Tramèr, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, Ú. Erlingsson, A. Oprea, and C. Raffel, “Extracting training data from large language models,” in 30th USENIX Security Symposium
2021
Cited alongside, same era.
B. Liu, M. Ding, S. Shaham, W. Rahayu, F. Farokhi, and Z. Lin, “When machine learning meets privacy: A survey and outlook,” ACM Computing Surveys
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
C. A. Choquette-Choo, F. Tramer, N. Carlini, and N. Papernot, “Label-only membership inference attacks,” in Proceedings of the 38th International Conference on Machine Learning
2021
Later among the works it cites.
Y. Kaya and T. Dumitras, “When does data augmentation help with membership inference attacks?,” in International Conference on Machine Learning
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
E. Dobriban and Y. Sheng, “Distributed linear regression by averaging,” The Annals of Statistics
2021
Later among the works it cites.