Web: http://arxiv.org/abs/2206.15407

Sept. 16, 2022, 1:12 a.m. | Andrey Malinin, Andreas Athanasopoulos, Muhamed Barakovic, Meritxell Bach Cuadra, Mark J. F. Gales, Cristina Granziera, Mara Graziani, Nikolay Kartash

cs.LG updates on arXiv.org arxiv.org

Distributional shift, or the mismatch between training and deployment data,
is a significant obstacle to the usage of machine learning in high-stakes
industrial applications, such as autonomous driving and medicine. This creates
a need to be able to assess how robustly ML models generalize as well as the
quality of their uncertainty estimates. Standard ML baseline datasets do not
allow these properties to be assessed, as the training, validation and test
data are often identically distributed. Recently, a range of …

arxiv dataset

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