J. Huggins, M. Kasprzak, T. Campbell, and T. Broderick. (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.
M. Vadera, A. Cobb, B. Jalaian, and B. Marlin. (2020)cite arxiv:2007.04466Comment: Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
J. Hron, A. Matthews, and Z. Ghahramani. Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, page 2019--2028. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)
C. Chu, K. Minami, and K. Fukumizu. (2020)cite arxiv:2004.01822Comment: ICLR 2020, Workshop on Integration of Deep Neural Models and Differential Equations.
S. Chatzis. Proceedings of the 30th International Conference on Machine Learning, volume 28 of Proceedings of Machine Learning Research, page 729--737. Atlanta, Georgia, USA, PMLR, (17--19 Jun 2013)
A. Foong, Y. Li, J. Hernández-Lobato, and R. Turner. (2019)cite arxiv:1906.11537Comment: Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning.
L. Ebner, P. Schwaferts, and T. Augustin. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, volume 103 of Proceedings of Machine Learning Research, page 167--174. Thagaste, Ghent, Belgium, PMLR, (03--06 Jul 2019)
J. De Bock, and G. de Cooman. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, volume 103 of Proceedings of Machine Learning Research, page 125--134. Thagaste, Ghent, Belgium, PMLR, (03--06 Jul 2019)
B. Cherief-Abdellatif. Proceedings of The 1st Symposium on Advances in Approximate Bayesian Inference, volume 96 of Proceedings of Machine Learning Research, page 11--31. PMLR, (02 Dec 2019)
J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer. (2018)cite arxiv:1805.12244Comment: Code available at https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos. v3: Expanded discussion, added Lotka-Volterra example. v4: Improved clarity.
H. Xing, G. Nicholls, and J. Lee. Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, page 6912--6920. Long Beach, California, USA, PMLR, (09--15 Jun 2019)
J. Lucas, G. Tucker, R. Grosse, and M. Norouzi. (2019)cite arxiv:1911.02469Comment: 11 main pages, 10 appendix pages. 13 figures total. Accepted at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).