Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp. 308-318, 2016.
Agarwal, N., Kale, S., Singh, K., and Thakurta, A. Differentially private and lazy online convex optimization. In The Thirty Sixth Annual Conference on Learning Theory, pp. 4599-4632. PMLR, 2023.
Balle, B. and Wang, Y.-X. Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising. In International Conference on Machine Learning, pp. 394-403. PMLR, 2018.
Balle, B., Barthe, G., and Gaboardi, M. Privacy amplification by subsampling: Tight analyses via couplings and divergences. In Advances in Neural Information Processing Systems, pp. 6277-6287, 2018.
Balle, B., Barthe, G., Gaboardi, M., Hsu, J., and Sato, T. Hypothesis testing interpretations and renyi differential privacy. In International Conference on Artificial Intelligence and Statistics, pp. 2496-2506. PMLR, 2020.
Bassily, R., Smith, A., and Thakurta, A. Private empirical risk minimization: Efficient algorithms and tight error bounds. In 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, pp. 464-473. IEEE, 2014.
Bun, M., Dwork, C., Rothblum, G. N., and Steinke, T. Composable and versatile privacy via truncated cdp. In Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, pp. 74-86, 2018.
Canonne, C. L., Kamath, G., and Steinke, T. The discrete gaussian for differential privacy. Advances in Neural Information Processing Systems, 33:15676-15688, 2020.
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D. Differentially private empirical risk minimization. Journal of Machine Learning Research, 12(3), 2011.
Dwork, C., McSherry, F., Nissim, K., and Smith, A. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pp. 265-284. Springer, 2006.
Gopi, S., Lee, Y. T., and Wutschitz, L. Numerical composition of differential privacy. In Advances in Neural Information Processing Systems, 2021.
Iyengar, R., Near, J. P., Song, D., Thakkar, O., Thakurta, A., and Wang, L. Towards practical differentially private convex optimization. In 2019 IEEE Symposium on Security and Privacy (SP), pp. 299-316. IEEE, 2019.
Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., and Xu, Z. Practical and private (deep) learning without sampling or shuffling. In International Conference on Machine Learning, pp. 5213-5225. PMLR, 2021.
Kifer, D., Smith, A., and Thakurta, A. Private convex empirical risk minimization and high-dimensional regression. In Conference on Learning Theory, pp. 25-1. JMLR Workshop and Conference Proceedings, 2012.
Koskela, A., Jälkö, J., and Honkela, A. Computing tight differential privacy guarantees using FFT. In International Conference on Artificial Intelligence and Statistics, pp. 2560-2569. PMLR, 2020.
Koskela, A., Jälkö, J., Prediger, L., and Honkela, A. Tight differential privacy for discrete-valued mechanisms and for the subsampled gaussian mechanism using FFT. In International Conference on Artificial Intelligence and Statistics, pp. 3358-3366. PMLR, 2021.
Kulkarni, J., Lee, Y. T., and Liu, D. Private non-smooth erm and sco in subquadratic steps. Advances in Neural Information Processing Systems, 34:4053-4064, 2021.