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. Brennan, and G. Bresler. (2020)cite arxiv:2005.08099Comment: 175 pages; subsumes preliminary draft arXiv:1908.06130; accepted for presentation at the Conference on Learning Theory (COLT) 2020.
Y. Yang, R. Bamler, and S. Mandt. (2020)cite arxiv:2006.04240Comment: 8 pages + detailed supplement with additional qualitative and quantitative results.
D. Scobee, and S. Sastry. (2019)cite arxiv:1909.05477Comment: Published as a conference paper at the International Conference on Learning Representations (ICLR), 2020 (at https://openreview.net/forum?id=BJliakStvH ).
S. Mukhopadhyay, and K. Wang. (2020)cite arxiv:2004.09588Comment: We'd love to hear your feedback. Email us. (We thank those who have already sent us their comments.).