N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer. Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP), pages 1897-1914. IEEE, 2022.
L. N. Darlow, E. J. Crowley, A. Antoniou, and A. J. Storkey. Cinic-10 is not imagenet or cifar-10. arXiv preprint arXiv:1810.03505, 2018.
F. Ding, M. Hardt, J. Miller, and L. Schmidt. Retiring adult: New datasets for fair machine learning. Advances in Neural Information Processing Systems, 34, 2021.
L. Grinsztajn, E. Oyallon, and G. Varoquaux. Why do tree-based models still outperform deep learning on tabular data? arXiv preprint arXiv:2207.08815, 2022.
V. Gupta, C. Jung, G. Noarov, M. M. Pai, and A. Roth. Online multivalid learning: Means, moments, and prediction intervals. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2022.
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arxiv 2015. arXiv preprint arXiv:1512.03385, 14, 2015.
U. Hébert-Johnson, M. Kim, O. Reingold, and G. Rothblum. Multicalibration: Calibration for the (computationally-identifiable) masses. In International Conference on Machine Learning, pages 1939–1948. PMLR, 2018.
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. Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays. PLOS Genetics, 4(8): 1-9, 08 2008. doi: 10.1371/journal.pgen.1000167. URL https://doi.org/10.1371/journal.pgen.1000167.
B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans. Revisiting membership inference under realistic assumptions. arXiv preprint arXiv:2005.10881, 2020.
B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans. Revisiting membership inference under realistic assumptions. Proceedings on Privacy Enhancing Technologies, 2021:348-368, 04 2021.
C. Jung, G. Noarov, R. Ramalingam, and A. Roth. Batch multivalid conformal prediction. In International Conference on Learning Representations (ICLR), 2023.
A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. 2009.
L. Li, K. Jamieson, A. Rostamizadeh, E. Gonina, J. Ben-Tzur, M. Hardt, B. Recht, and A. Talwalkar. A system for massively parallel hyperparameter tuning. Proceedings of Machine Learning and Systems, 2:230-246, 2020.
Z. Li and Y. Zhang. Membership leakage in label-only exposures. Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2020.
R. Liaw, E. Liang, R. Nishihara, P. Moritz, J. E. Gonzalez, and I. Stoica. Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118, 2018.
Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022.
Y. Long, V. Bindschaedler, L. Wang, D. Bu, X. Wang, H. Tang, C. A. Gunter, and K. Chen. Understanding membership inferences on well-generalized learning models. CoRR, abs/1802.04889, 2018. URL http://arxiv.org/abs/1802.04889.
Y. Long, L. Wang, D. Bu, V. Bindschaedler, X. Wang, H. Tang, C. A. Gunter, and K. Chen. A pragmatic approach to membership inferences on machine learning models. In 2020 IEEE European Symposium on Security and Privacy (EuroS&P), pages 521-534. IEEE, 2020.
J. Neyman and E. S. Pearson. On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society A, 231:289-337, 1933.