A View on Model Misspecification in Uncertainty Quantification

Author:

Kato Yuko,Tax David M. J.,Loog Marco

Publisher

Springer Nature Switzerland

Reference44 articles.

1. Hie, B., Bryson, B.D., Berger, B.: Leveraging uncertainty in machine learning accelerates biological discovery and design. Cell Syst. 11, 461–477 (2020)

2. Vishwakarma, G., Sonpal, A., Hachmann, J.: Metrics for benchmarking and uncertainty quantification: quality, applicability, and best practices for machine learning in chemistry. Trends Chem. 3, 146–156 (2021)

3. Begoli, E., Bhattacharya, T., Kusnezov, D.: The need for uncertainty quantification in machine-assisted medical decision making. Nat. Mach. Intell. 1, 20–23 (2019)

4. Michelmore, R., Kwiatkowska, M., Gal, Y.: Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control. arXiv: 1811.06817 (2018)

5. Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

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