[59] Samuel Livingstone, Michael Betancourt, Simon Byrne, and Mark Girolami. On the geometric ergodicity of hamiltonian Monte Carlo. 2019.
[60] Lauri Lode et al. Sub-sampled and differentially private hamiltonian Monte Carlo. 2019.
[61] Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, Chris Oates, et al. Robust generalised Bayesian inference for intractable likelihoods. arXiv preprint arXiv:2104.07359, 2021.
[62] H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz. A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210, 2018.
[63] Frank McSherry and Kunal Talwar. Mechanism design via differential privacy. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), pages 94-103. IEEE, 2007.
[64] Minami Mihoko and Shinto Eguchi. Robust blind source separation by beta divergence. Neural computation, 14(8):1859-1886, 2002.
[65] Jeffrey W Miller. Asymptotic normality, concentration, and coverage of generalized posteriors. The Journal of Machine Learning Research, 22(1):7598-7650, 2021.
[66] Kentaro Minami, HItomi Arai, Issei Sato, and Hiroshi Nakagawa. Differential privacy without sensitivity. Advances in Neural Information Processing Systems, 29, 2016.
[67] Darakhshan J Mir. Differential privacy: an exploration of the privacy-utility landscape. Rutgers The State University of New Jersey, School of Graduate Studies, 2013.
[68] Ilya Mironov. Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF), pages 263-275. IEEE, 2017.
[69] Milad Nasr, Shuang Songi, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlin. Adversary instantiation: Lower bounds for differentially private machine learning. In 2021 IEEE Symposium on security and privacy (SP), pages 866-882. IEEE, 2021.
[70] Radford M Neal. Bayesian learning for neural networks, volume 118. Springer Science & Business Media, 2012.
[71] Mijung Park, James Foulds, Kamalika Chaudhuri, and Max Welling. VariationalBayes in private settings (vips). Journal of Artificial Intelligence Research, 68:109-157, 2020.
[72] Melanie F Pradier, Niranjani Prasad, Paidamoyo Chapfuwa, Sahara Ghalebikesabi, Max Ilse, Steven Woodhouse, Rebecca Elyanow, Javier Zazo, Javier Gonzalez, Julia Greissl, et al. Airiva: A deep generative model of adaptive immune repertoires. arXiv preprint arXiv:2304.13737, 2023.
[73] Ossi Räisä, Antti Koskela, and Antti Honkela. Differentially private hamiltonian Monte Carlo. arXiv preprint arXiv:2106.09376, 2021.
[74] Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou. White-box vs black-box: Bayes optimal strategies for membership inference. In International Conference on Machine Learning, pages 5558-5567. PMLR, 2019.
[75] Jeremy Seeman, Matthew Reimherr, and Aleksandra Slavković. Exact privacy guarantees for Markov chain implementations of the exponential mechanism with artificial atoms. Advances in Neural Information Processing Systems, 34:13125-13136, 2021.
[76] Daniel Seita, Xinlei Pan, Haoyu Chen, and John Canny. An efficient minibatch acceptance test for Metropolis-Hastings. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, pages 5359-5363, 2018.
[77] Adam Smith. Privacy-preserving statistical estimation with optimal convergence rates. In Proceedings of the forty-third annual ACM symposium on Theory of computing, pages 813-822, 2011.
[78] Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso. Synthetic data-anonymisation groundhog day. In 31st USENIX Security Symposium (USENIX Security 22), pages 1451-1468, 2022.