[29] Sanket Kamthe, Samuel A. Assefa, and Marc Peter Deisenroth. Copula flows for synthetic data generation. ArXiv, abs/2101.00598, 2021.
[30] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4396-4405, 2018.
[31] Diederik P. Kingma and Max Welling. Auto-Encoding Variational Bayes. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014.
[32] Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. Semi-supervised learning with deep generative models. In Advances in neural information processing systems, pages 3581-3589, 2014.
[33] Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. Improved variational inference with inverse autoregressive flow. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016.
[34] A Kiran and S Saravana Kumar. A comparative analysis of gan and vae based synthetic data generators for high dimensional, imbalanced tabular data. In 2023 2nd International Conference for Innovation in Technology (INOCON), pages 1-6. IEEE, 2023.
[35] Roger W. Koenker, Limin Peng, Victor Chernozhukov, and Xuming He. Handbook of quantile regression. 2017.
[36] Alex Krizhevsky. Learning multiple layers of features from tiny images. 2009.
[37] Abhishek Kumar and Ben Poole. On implicit regularization in  $\beta$ -vaes. In International Conference on Machine Learning, pages 5480-5490. PMLR, 2020.
[38] Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. Autoencoding beyond pixels using a learned similarity metric. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 1558-1566, New York, New York, USA, 20-22 Jun 2016. PMLR.
[39] Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine. Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 741-752. Curran Associates, Inc., 2020.
[40] Erich Leo Lehmann. Elements of large-sample theory. 1998.
[41] James Lucas, G. Tucker, Roger Baker Grosse, and Mohammad Norouzi. Don't blame the elbo! a linear vae perspective on posterior collapse. In Neural Information Processing Systems, 2019.
[42] Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. Adversarial autoencoders. arXiv preprint arXiv:1511.05644, 2015.
[43] James E. Matheson and Robert L. Winkler. Scoring rules for continuous probability distributions. Management Science, 22:1087-1096, 1976.
[44] Stan Matwin, Jordi Nin, Morvarid Sehatkar, and Tomasz Szapiro. A review of attribute disclosure control. In Advanced Research in Data Privacy, 2015.
[45] Sangjun Moon, Jong-June Jeon, Jason Eng Hun Lee, and Yongdai Kim. Learning multiple quantiles with neural networks. Journal of Computational and Graphical Statistics, 30:1238-1248, 2021.
[46] Prateek Munjal, Akanksha Paul, and N. C. Krishnan. Implicit discriminator in variational autoencoder. 2020 International Joint Conference on Neural Networks (IJCNN), pages 1-8, 2019.
[47] Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim. Data synthesis based on generative adversarial networks. Proc. VLDB Endow., 11:1071-1083, 2018.
[48] V. Parmar, Bogdan Penkovsky, Damien Querlioz, and Manan Suri. Hardware-efficient stochastic binary cnn architectures for near-sensor computing. Frontiers in Neuroscience, 15, 2022.