15 citations · 22 across the 5 of their papers we have counts for
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cs.LG2023★ 2 cited
Insights From Insurance for Fair Machine Learning
Christian Fröhlich, Robert C. Williamson
We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and…
cs.LG2022★ 4 cited
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
Christian Fröhlich, Robert C. Williamson
Expected risk minimization (ERM) is at the core of many machine learning systems. This means that the risk inherent in a loss distribution is summarized using a single number - its…
cs.LG2022★ 1 cited
Risk Measures and Upper Probabilities: Coherence and Stratification
Christian Fröhlich, Robert C. Williamson
Machine learning typically presupposes classical probability theory which implies that aggregation is built upon expectation. There are now multiple reasons to motivate looking at…