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J. Huggins, M. Kasprzak, T. Campbell, und 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, und 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.
M. Vadera, A. Cobb, B. Jalaian, und B. Marlin. (2020)cite arxiv:2007.04466Comment: Presented at the ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
M. Brennan, und 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, und S. Mandt. (2020)cite arxiv:2006.04240Comment: 8 pages + detailed supplement with additional qualitative and quantitative results.
D. Burt, C. Rasmussen, und M. Van Der Wilk. Proceedings of the 36th International Conference on Machine Learning, Volume 97 von Proceedings of Machine Learning Research, Seite 862--871. Long Beach, California, USA, PMLR, (09--15 Jun 2019)
D. Diochnos, S. Mahloujifar, und 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.
J. Hron, A. Matthews, und Z. Ghahramani. Proceedings of the 35th International Conference on Machine Learning, Volume 80 von Proceedings of Machine Learning Research, Seite 2019--2028. Stockholmsmässan, Stockholm Sweden, PMLR, (10--15 Jul 2018)