[86] G. Roeder, Y. Wu, and D. Duvenaud. Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 6925-6934, 2017.
[87] S. Rossi, P. Michiardi, and M. Filippone. Good Initializations of Variational Bayes for Deep Models. In K. Chaudhuri and R. Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 5487-5497. PMLR, 2019.
[88] S. Rossi, S. Marmin, and M. Filippone. Walsh-Hadamard Variational Inference for Bayesian Deep Learning. In Advances in Neural Information Processing Systems 33, 2020.
[89] T. G. J. Rudner, Z. Chen, Y. W. Teh, and Y. Gal. Tractable Function-Space Variational Inference in Bayesian Neural Networks. In A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, editors, Advances in Neural Information Processing Systems, 2022.
[90] F. R. Ruiz, M. Titsias, and D. Blei. The Generalized Reparameterization Gradient. 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.
[91] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning Representations by Backpropagating Errors. 323(6088):533-536, 1986-10.
[92] T. Salimans and D. A. Knowles. Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression. 8(4):837–882, 2013.
[93] H. Salimbeni and M. Deisenroth. Doubly Stochastic Variational Inference for Deep Gaussian Processes. In Advances in Neural Information Processing Systems 30, pages 4588-4599. Curran Associates, Inc., 2017.
[94] B. Simsek, F. Ged, A. Jacot, F. Spadaro, C. Hongler, W. Gerstner, and J. Brea. Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 9722-9732. PMLR, 18-24 Jul 2021.
[95] E. Snelson and Z. Ghahramani. Sparse Gaussian Processes using Pseudo-Inputs. In Advances in Neural Information Processing Systems 18, pages 1257–1264. MIT Press, 2006.
[96] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[97] J. Song, C. Meng, and S. Ermon. Denoising Diffusion Implicit Models. In International Conference on Learning Representations, 2021.
[98] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-Based Generative Modeling through Stochastic Differential Equations. In International Conference on Learning Representations, 2021.
[99] S. Sun, G. Zhang, J. Shi, and R. B. Grosse. Functional Variational Bayesian Neural Networks. In Proceedings of the 7th International Conference on Learning Representations. OpenReview.net, 2019.
[100] A. Takatsu. Wasserstein geometry of Gaussian measures. Osaka Journal of Mathematics, 48 (4):1005 - 1026, 2011.
[101] Tishby, Levin, and Solla. Consistent inference of probabilities in layered networks: predictions and generalizations. In International 1989 Joint Conference on Neural Networks, pages 403-409 vol.2, 1989.
[102] B.-H. Tran, S. Rossi, D. Milios, P. Michiardi, E. V. Bonilla, and M. Filippone. Model Selection for Bayesian Autoencoders. In Advances in Neural Information Processing Systems, volume 34, pages 19730-19742. Curran Associates, Inc., 2021.
[103] B.-H. Tran, S. Rossi, D. Milios, and M. Filippone. All You Need is a Good Functional Prior for Bayesian Deep Learning. In The Journal of Machine Learning Research, 2022.
[104] C. Villani. Optimal Transport: Old and New, volume 338. Springer Science & Business Media, 2008.