J. Mukhoti, A. Kirsch, J. vanAmersfoort, P. H. S. Torr, and Y. Gal. Deep deterministic uncertainty: A simple baseline. *ArXiv*, 2102.11582, 2021.
R. Neal. Bayesian Learning for Neural Networks. Springer Verlag, New York, 1996.
I. Oleksiienko, D. T. Tran, and A. Iosifidis. Variational neural networks. ArXiv, 2027.01524, 2022.
I. Osband, Z. Wen, S. M. Asghari, V. Dwaracherla, M. Ibrahimi, X. Lu, and B. VanRoy. Weight uncertainty in neural networks. *ArXiv*, 2107.08924, 2021.
Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, and J. Snoek. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. In Advances in Neural Information Processing Systems, volume 32, 2019.
A. Owen and Y. Zhou. Safe and effective importance sampling. Journal of the American Statistical Association, 95(449):135-143, 2000.
J. Parker-Holder, L. Metz, C. Resnick, H. Hu, A. Lerer, A. Letcher, A. Peysakhovich, A. Pacchiano, and J. Foerster. Ridge rider: Finding diverse solutions by following eigenvectors of the hessian. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 753-765. Curran Associates, Inc., 2020.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 2019.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Preetenhofer, R. Weiss, V. Dubourg, et al. Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12:2825-2830, 2011.
J. Postels, M. Segu, T. Sun, L. Van Gool, F. Yu, and F. Tombari. On the practicality of deterministic epistemic uncertainty. *ArXiv*, 2107.00649, 2021.
A. E. Raftery and L. Bao. Estimating and projecting trends in HIV/AIDS generalized epidemics using incremental mixture importance sampling. Biometrics, 66, 2010.
M. Rigter, B. Lacerda, and N. Hawes. Rambo-rl: Robust adversarial model-based offline reinforcement learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 16082-16097. Curran Associates, Inc., 2022.
L. Scimeca, S. J. Oh, S. Chun, M. Poli, and S. Yun. Which shortcut cues will dnns choose? a study from the parameter-space perspective. arXiv, 2110.03095, 2022.
T. Seidenfeld. Entropy and uncertainty. Philosophy of Science, 53(4):467-491, 1986.
H. Shah, K. Tamuly, A. Raghunathan, P. Jain, and P. Netrapalli. The pitfalls of simplicity bias in neural networks. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 9573-9585. Curran Associates, Inc., 2020.
C. E. Shannon and C. Elwood. A mathematical theory of communication. The Bell System Technical Journal, 27:379-423, 1948.
L. Smith and Y. Gal. Understanding measures of uncertainty for adversarial example detection. In A. Globerson and R. Silva, editors, Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, pages 560-569. AUAI Press, 2018.
R. J. Steele, A. E. Raftery, and M. J. Emond. Computing normalizing constants for finite mixture models via incremental mixture importance sampling (IMIS). Journal of Computational and Graphical Statistics, 15, 2006.
M. Sundararajan, A. Taly, and Q. Yan. Axiomatic attribution for deep networks. In International Conference on Machine Learning, 2017.