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.
S. Chen, E. Dobriban, and J. Lee. (2019)cite arxiv:1907.10905Comment: Changed title. Added results on overparametrized 2-layer nets. Added error bars to experiments. Numerous other minor improvements.
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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.
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.
D. Diochnos, S. Mahloujifar, and M. Mahmoody. (2018)cite arxiv:1810.12272Comment: Full version of a work with the same title that will appear in NIPS 2018, 31 pages containing 5 figures, 1 table, 2 algorithms.
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