V. Papyan, J. Sulam, and M. Elad. (2017)cite arxiv:1707.06066Comment: This is the journal version of arXiv:1607.02005 and arXiv:1607.02009, accepted to IEEE Transactions on Signal Processing.
B. Ghojogh, M. Sikaroudi, H. Tizhoosh, F. Karray, and M. Crowley. (2020)cite arxiv:2004.01857Comment: Accepted (to appear) in International Conference on Image Analysis and Recognition (ICIAR) 2020, Springer.
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra. Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, page 1613--1622. Lille, France, PMLR, (07--09 Jul 2015)
A. Fisher, and E. Kennedy. (2018)cite arxiv:1810.03260Comment: This manuscript version includes 2 additional supplemental figures to further aid intuition. In total: 4 figures, 36 pages (double spaced).
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein. (2017)cite arxiv:1712.09913Comment: NIPS 2018 (extended version, 10.5 pages), code is available at https://github.com/tomgoldstein/loss-landscape.
A. Kumar, P. Liang, and T. Ma. (2019)cite arxiv:1909.10155Comment: Accepted as a spotlight to NeurIPS 2019, original title was "Variance Reduced Uncertainty Calibration".
O. Montasser, S. Hanneke, and N. Srebro. Proceedings of the Thirty-Second Conference on Learning Theory, volume 99 of Proceedings of Machine Learning Research, page 2512--2530. Phoenix, USA, PMLR, (25--28 Jun 2019)
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)
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.