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Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not.
On the problem of the most efficient tests of statistical hypotheses
J. Neyman and E. S. Pearson · 1933
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J. Bergstra, D. Yamins, and D. Cox · 2013
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Deep residual learning for image recognition. arxiv 2015
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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S. Zagoruyko and N. Komodakis · 2016
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Multicalibration: Calibration for the (computationally-identifiable) masses
U. Hébert-Johnson, M. Kim, O. Reingold, and G. Rothblum · 2018
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Tune: A research platform for distributed model selection and training
R. Liaw, E. Liang, R. Nishihara, P. Moritz, J. E. Gonzalez, and I. Stoica · 2018
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Understanding membership inferences on well-generalized learning models
Y. Long, V. Bindschaedler, L. Wang, D. Bu, X. Wang, H. Tang, C. A. Gunter, and K. Chen · 2018
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A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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Optuna: A next-generation hyperparameter optimization framework
T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, and H. Jégou · 2019
Cited alongside, same era.
Revisiting membership inference under realistic assumptions
B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans · 2020
Cited alongside, same era.
On the importance of difficulty calibration in membership inference attacks
L. Watson, C. Guo, G. Cormode, and A. Sablayrolles · 2021
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Enhanced membership inference attacks against machine learning models
J. Ye, A. Maddi, S. K. Murakonda, and R. Shokri · 2021
Later among the works it cites.
Practical adversarial multivalid conformal prediction
O. Bastani, V. Gupta, C. Jung, G. Noarov, R. Ramalingam, and A. Roth · 2022
Later among the works it cites.
Membership inference attacks from first principles
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer · 2022
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Why do tree-based models still outperform deep learning on tabular data?
L. Grinsztajn, E. Oyallon, and G. Varoquaux · 2022
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A system for massively parallel hyperparameter tuning
L. Li, K. Jamieson, A. Rostamizadeh, E. Gonina, J. Ben-Tzur, M. Hardt, B. Recht, and A. Talwalkar · 2020
Cited alongside, same era.
Membership leakage in label-only exposures
Z. Li and Y. Zhang · 2020
Cited alongside, same era.
A pragmatic approach to membership inferences on machine learning models
Y. Long, L. Wang, D. Bu, V. Bindschaedler, X. Wang, H. Tang, C. A. Gunter, and K. Chen · 2020
Cited alongside, same era.
Retiring adult: New datasets for fair machine learning
F. Ding, M. Hardt, J. Miller, and L. Schmidt · 2021
Cited alongside, same era.
Revisiting membership inference under realistic assumptions
B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans · 2021
Cited alongside, same era.
Systematic evaluation of privacy risks of machine learning models
L. Song and P. Mittal · 2021
Cited alongside, same era.
Online multivalid learning: Means, moments, and prediction intervals
V. Gupta, C. Jung, G. Noarov, M. M. Pai, and A. Roth · 2022
Later among the works it cites.
A convnet for the 2020s
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie · 2022
Later among the works it cites.
Uncertain: Modern topics in uncertainty estimation
A. Roth · 2022
Later among the works it cites.
Batch multivalid conformal prediction
C. Jung, G. Noarov, R. Ramalingam, and A. Roth · 2023
Closest in time.
The scope of multicalibration: Characterizing multicalibration via property elicitation
G. Noarov and A. Roth · 2023
Closest in time.
Canary in a coalmine: Better membership inference with ensembled adversarial queries
Y. Wen, A. Bansal, H. Kazemi, E. Borgnia, M. Goldblum, J. Geiping, and T. Goldstein · 2023
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