Web: http://arxiv.org/abs/2206.11116

June 23, 2022, 1:11 a.m. | Mohammed Naveed Akram, Akshatha Ambekar, Ioannis Sorokos, Koorosh Aslansefat, Daniel Schneider

cs.LG updates on arXiv.org arxiv.org

Reliability estimation of Machine Learning (ML) models is becoming a crucial
subject. This is particularly the case when such \mbox{models} are deployed in
safety-critical applications, as the decisions based on model predictions can
result in hazardous situations. In this regard, recent research has proposed
methods to achieve safe, \mbox{dependable}, and reliable ML systems. One such
method consists of detecting and analyzing distributional shift, and then
measuring how such systems respond to these shifts. This was proposed in
earlier work in …

arxiv forecasting lg ml reliability robustness statistical

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